Detecting potential significant errors in speech recognition results

ABSTRACT

In some embodiments, recognition results produced by a speech processing system (which may include two or more recognition results, including a top recognition result and one or more alternative recognition results) based on an analysis of a speech input, are evaluated for indications of potential errors. In some embodiments, the indications of potential errors may include discrepancies between recognition results that are meaningful for a domain, such as medically-meaningful discrepancies. The evaluation of the recognition results may be carried out using any suitable criteria, including one or more criteria that differ from criteria used by an ASR system in determining the top recognition result and the alternative recognition results from the speech input. In some embodiments, a recognition result may additionally or alternatively be processed to determine whether the recognition result includes a word or phrase that is unlikely to appear in a domain to which speech input relates.

CROSS REFERENCE TO RELATED APPLICATIONS

This Application claims the benefit under 35 U.S.C. § 120 as acontinuation of U.S. application Ser. No. 14/713,372, entitled“DETECTING POTENTIAL SIGNIFICANT ERRORS IN SPEECH RECOGNITION RESULTS”filed on May 15, 2015, now U.S. Pat. No. 9,818,398, which is hereinincorporated by reference in its entirety. U.S. application Ser. No.14/713,372, now U.S. Pat. No. 9,818,398, claims the benefit under 35U.S.C. § 120 as a continuation of U.S. application Ser. No. 13/544,215,entitled “DETECTING POTENTIAL SIGNIFICANT ERRORS IN SPEECH RECOGNITIONRESULTS” filed on Jul. 9, 2012, now U.S. Pat. No. 9,064,492, which isherein incorporated by reference in its entirety.

BACKGROUND

Automatic speech recognition (ASR) systems can be used to process inputspeech to yield a recognition result corresponding to the speech. Therecognition result may be a text transcription of the speech. ASRsystems can be used in many different contexts for processing speechregarding a variety of domains.

The recognition results of ASR systems may include misrecognition errorsfor any of a variety of reasons. For example, the errors may result fromlow-quality audio input, such as from faulty audio capture hardware,from unclear speech from a speaker, or from errors in analysis conductedby the ASR system.

SUMMARY

In one embodiment, there is provided a method of processing results of arecognition by an automatic speech recognition (ASR) system on a speechinput. The results comprise two or more results identified by the ASRsystem as likely to be an accurate recognition result for the speechinput. The method comprises evaluating the two or more results using atleast one criterion that differs from criteria used by the ASR system indetermining the two or more result and, in response to determining thatthe at least one criterion is met by the two or more results, triggeringan alert concerning one of the two or more results.

In another embodiment, there is provided at least one computer-readablestorage medium having encoded thereon computer-executable instructionsthat, when executed by at least one computer, cause the at least onecomputer to carry out a method of processing results of a recognition byan automatic speech recognition (ASR) system on a speech input. Theresults comprise two or more results identified by the ASR system aslikely to be an accurate recognition result for the speech input. Themethod comprises evaluating the two or more results using at least onecriterion that differs from criteria used by the ASR system indetermining the two or more results and, in response to determining thatthe at least one criterion is met by the two or more results, triggeringan alert concerning one of the two or more results.

In a further embodiment, there is provided a method of processing aresult of a recognition by an automatic speech recognition (ASR) systemon a speech input. The method comprises determining whether the resultfrom the ASR system comprises a word or phrase that is unlikely to occurin a domain to which the speech input relates and, in response todetermining that the result comprises a word or phrase that is unlikelyto occur in the domain to which the speech input relates, triggering analert for the result. The foregoing is a non-limiting summary of theinvention, which is defined by the attached claims.

BRIEF DESCRIPTION OF DRAWINGS

The accompanying drawings are not intended to be drawn to scale. In thedrawings, each identical or nearly identical component that isillustrated in various figures is represented by a like numeral. Forpurposes of clarity, not every component may be labeled in everydrawing. In the drawings:

FIG. 1 is a block diagram of some components of a speech processingsystem with which some embodiments may act;

FIG. 2 is a flowchart of an exemplary process that may be carried out insome embodiments to identify potential significant errors in recognitionresults;

FIG. 3 is a flowchart of an exemplary process that may be carried out insome embodiments to identify potential significant errors based on asemantic interpretation of recognition results;

FIG. 4 is a flowchart of an exemplary process that may be carried out insome embodiments to identify potential significant errors based onwhether both words of a word pair appear in recognition results;

FIG. 5 is a flowchart of an exemplary process that may be carried out insome embodiments to identify potential significant errors based onwhether, when a word of a word pair appears in the top recognitionresult, a language model indicates that the other word of the word pairwas sufficiently likely to have appeared instead in the speech input;

FIG. 6 is a flowchart of an exemplary process that may be carried out insome embodiments to identify potential significant errors in recognitionresults based at least in part on a language model and a word pair whenthe word pair includes a null word;

FIG. 7 is a flowchart of an exemplary process that may be carried out insome embodiments to create a set of words useable to evaluaterecognition results to determine whether there are indications ofpotential significant errors in the recognition results;

FIG. 8 is a flowchart of an exemplary process that may be carried out insome embodiments to identify potential significant errors based onwhether a top recognition result for speech in a domain includes wordsthat are unlikely to appear in recognition results for the domain;

FIG. 9 is a flowchart of an exemplary process that may be carried out insome embodiments to determine a likelihood that a speaker who providedspeech input was uncertain when providing the speech input;

FIG. 10 is a flowchart of an exemplary process that may be carried outin some embodiments to display to a reviewer information regardingrecognition results and potential significant errors; and

FIG. 11 is a block diagram of an exemplary computing device on whichembodiments described herein may be implemented.

DETAILED DESCRIPTION

Correction of errors in a recognition result from an ASR system can becarried out by a reviewer of the result. The reviewer may be the speakeror any other suitable review entity (human or otherwise) that reviewsthe results in any of various ways, such as by viewing a textualtranscription of the speech. In one review process, a human reviewerviews a recognition result on a display screen while listening to audiofrom which the recognition result was generated, compares the audio tothe recognition result, and corrects any errors found by the reviewer.

Applicants have recognized and appreciated that review processes aredifficult and laborious for reviewers. It is challenging for reviewersto carefully proofread a recognition result and pick out errors in theresult, particularly when there are only a small number of errors in alarge recognition result or in multiple recognition results reviewedtogether. Review processes are therefore also not wholly reliable, asthe reviewing may be subject to errors. For example, a process may beprone to human error when the speech relates to a specialized ortechnical topic with which a human reviewer may be unfamiliar, such thatthe reviewer may fail to detect some errors. As another example, errorsin the recognition results may be difficult for a reviewer to detectwhen the errors result from the ASR system not identifying, ormisidentifying, very brief audio segments, resulting in an error in theresults that a reviewer may also miss. This could be the case witherrors such as a recognition result including the word “typical” inplace of the correct word “atypical” due to a misrecognition of theshort audio segment corresponding to the “a” sound in the speech thatwas input to the ASR system.

Applicants have recognized and appreciated that some misrecognitionerrors (e.g., errors from incorrectly identifying a sound in speechinput or failing to identify a sound in speech input) in a recognitionresult generated by an ASR system may be significant, in that the errorsmay have serious consequences if not identified and corrected. Forexample, when an ASR system is processing speech in a medical contextand the recognition result corresponding to speech contains errors, theerrors could have serious medical consequences. An example of acircumstance under which such a serious medical consequence may resultfrom a misrecognition is when, for example, a clinician is dictating aresult of a medical test, such as a radiological test, and an error inthe recognition result changes the meaning of what the clinician said ina medically significant way. When the meaning of the recognition resultchanges in a medically significant way as a result of an error, theresulting medical record may be incorrect in a medically significantway, which may, for example, result in the wrong diagnosis or treatmentof a patient because of the error. Such a change in the diagnosis andtreatment may be problematic for the patient. For example, a patient maybe treated for a disease or condition that the patient does not have ornot be properly treated for a disease or condition that the patient doeshave.

Applicants have further recognized and appreciated that misrecognitionof a speech input that results in a recognition result having asignificantly different meaning than the speech input may occur as aresult of an incorrect recognition of one or more words or a failure torecognize one or more words. Such errors might derive from the ASRsystem misrecognizing (which, as used herein, can include incorrectlyrecognizing and/or failing to recognize) short sounds of one or a fewsyllables. For example, misrecognition of a word may occur due tomisrecognition of a portion of the word, such as a prefix or suffix thatis only one or a few syllables. A misrecognition of a word may alsooccur due to the ASR system missing a word that includes only one or afew syllables. Examples of such errors in the medical context includemisrecognizing “malignant” as “nonmalignant” (or vice versa), whichcould lead to erroneous recognition results like “the growth ismalignant” when nonmalignant was spoken or “the growth is non-malignant”when malignant was spoken. Another example includes missing the word“no” in a phrase such as “there is no evidence of active disease in theabdomen,” leading to the erroneous recognition result “there is evidenceof active disease in the abdomen.”

In many ASR systems, the recognition result returned is the “best guess”of the ASR system of text that corresponds to a speech input, and thatthe ASR system may have produced one or more other alternativerecognition results that the ASR system identified as possibly correct.Many ASR systems operate based on probabilistic processes, such that theASR system may produce multiple possible recognition results, each ofwhich is associated with a probability of being a correct output. A toprecognition result produced by the ASR system for the speech input maybe the result that the probabilities indicate is most likely to becorrect. Applicants have recognized and appreciated that when the topresult of the ASR system includes the types of errors discussed above,in some cases one or more of the alternative recognition resultsproduced by the ASR system may be more accurate in some ways and notinclude one or more of the errors. Continuing to use the examples above,while the top recognition result that the ASR system identified as mostlikely to be correct may erroneously include the word “malignant,” insome cases one or more of the alternative results that were identifiedby the ASR system as possible (but less likely to be correct)recognition results may correctly include the word “nonmalignant.”

Differences between the contents of a top recognition result andalternative recognition results of an ASR system therefore mightcorrespond to an error in the top recognition result. When thedifferences correspond to the types of errors that have seriousconsequences, the differences may be indicative of one or more potentialsignificant errors in the top result. Thus, for speech input in themedical domain, an evaluation of the top recognition result and thealternative recognition results may identify discrepancies between theresults that result in meanings of the results being different in amedically-significant way, which may be indicative of potentialmedically-significant errors in the top recognition result. In otherdomains, an evaluation of the top recognition result and the alternativerecognition results may identify results having meanings that aredifferent in a way that is significant for the domain.

Applicants have therefore recognized and appreciated that a comparisonof the top result to the alternative recognition results may aid inidentifying potential errors in the top result that would have seriousconsequences if not identified and corrected. Applicants have alsorecognized and appreciated that performing such a comparison and callingthe attention of the reviewer to the identified potential significanterrors in the top result may aid the reviewer in carrying out a reviewprocess. In particular, identifying potential significant errors to areviewer may help ensure that reviewers pay attention to potentialsignificant errors in a recognition result and help ensure that thereviewers correct the errors in results of an ASR system that mightotherwise have serious consequences.

In accordance with some aspects of the invention described herein, therecognition results, produced by a speech processing system (which mayinclude two or more recognition results, including a top recognitionresult and one or more alternative recognition results) based on ananalysis of a speech input, are evaluated for indications of potentialsignificant errors. The indications of potential significant errors mayinclude discrepancies between recognition results that are meaningfulfor a domain, such as medically-meaningful discrepancies. The evaluationof the recognition results may be carried out using any suitablecriteria. In some embodiments, the criteria include one or more criteriathat differ from criteria used by an ASR system in determining the toprecognition result and the alternative recognition results from thespeech input.

As a first example of the way in which embodiments may evaluate therecognition results, the recognition results may be evaluated todetermine whether a meaning of any of the alternative recognitionresults differs from a meaning of the top recognition result in a mannerthat is significant for the domain of the speech input (e.g., themedical domain discussed above or some other domain). A significantdifference in meaning between recognition results could be indicative ofa potential significant error in the top recognition result.

As a second example, the recognition results could be evaluated usingone or more sets of words and/or phrases, such as pairs ofwords/phrases. Each set of words and/or phrases may includewords/phrases that are acoustically similar to one another and/or thatan ASR system may confuse for one another during a speech recognitionprocess. Further, the sets of words/phrases may include words/phrasesthat, when incorrectly included in a result, would change a meaning ofthe result in a manner that would be significant for the domain. Anexample of one such set of words is “malignant” and “nonmalignant,”which may be confused for one another by an ASR system and that, asdiscussed above, could have serious consequences when misrecognized andincorrectly included in speech recognition results in a medical domain.The recognition results may be evaluated using the set(s) ofwords/phrases to determine, when the top result includes a word/phrasefrom a set of words/phrases, whether any of the alternative recognitionresults includes any of the other, corresponding words/phrases from theset. When the top result includes one of the words/phrases of a set andan alternative result includes one of the other words/phrases from theset, this may be indicative of a potential error in the top recognitionresult that may change the meaning of the result in a way that may besignificant for the domain.

As a third example, such sets of words/phrases that may be acousticallysimilar or otherwise confusable, the misrecognition of which can besignificant in the domain, may be used together with an acoustic modelor a language model to evaluate a top recognition result to determinewhether the top recognition result includes potential significanterrors. For example, the top recognition result may be evaluated usingsets of words/phrases to determine whether any of the words/phrasesappear in the result. If any of a set of words/phrases appear, then itis determined whether it is likely that the word/phrase appears in errorand that a correct recognition of the speech input should insteadinclude another member of the set of words/phrases (i.e., it isdetermined whether it is sufficiently likely that the speech inputincluded the other member of the set for an action to be taken). To makethe determination, the word/phrase of the set that appears in the resultmay be iteratively replaced with each of the other words/phrases of theset. The string of words that results from each of the replacements maythen be evaluated using a language model to determine a likelihood ofthe newly-created string of words appearing in the language and/ordomain to which the language model corresponds. The likelihood producedby the language model may then be evaluated to determine whether thenewly-created string of words is sufficiently likely to appear for analert to be triggered. To determine whether the newly-created string ofwords is sufficiently likely, the likelihood of the newly-created stringof words appearing in the language and/or domain may be compared to athreshold, compared to likelihoods of other strings of words, orotherwise evaluated. If an evaluation of the likelihood produced by thelanguage model indicates a newly-created string of words is notsufficiently likely to appear in the language, then it may be determinedthat it is unlikely that the speech input includes the word/phrase thatwas inserted to create the string and no alert may be triggered.However, if the string of words is sufficiently likely to appear in thelanguage (e.g., when the likelihood exceeds a threshold), then theword/phrase from the set that was inserted into the result may also besufficiently likely to appear in the speech input at the position atwhich it was inserted. When the inserted word is sufficiently likely tohave appeared in the speech input, this indicates that the appearance ofthe word/phrase from the set in the top result is a potentialsignificant error in the top result (e.g., because the speech input mayhave instead included the other word from the set). The process ofinserting words/phrases from the set into the top recognition result andevaluating the resulting string of words using the language model may berepeated for each word/phrase in the set. If the result of the analysisusing the language model indicates that none of the strings of wordsincluding the other words/phrases from the set are sufficiently likelyto have appeared in the speech input, then it may be determined that theappearance of the word/phrase of the set in the top recognition resultis not a potential significant error.

In some embodiments that evaluate recognition results in accordance withthis third example, the language model may provide a likelihood of thecreated string of words appearing in the language to which the modelcorresponds, with the likelihood not being edited or adjusted in anyway. In other embodiments, however, the likelihood may be adjustedaccording to the significance of consequences that may occur in a domainif the string of words is incorrect (e.g., the significance ofconsequences of misrecognizing “malignant”). In some such embodiments, alikelihood of a string of words appearing may be weighted according tothe significance, such that, for example, a likelihood may be higherwhen misrecognition would have serious consequences. In this way, if astring of words appears infrequently in the language but would haveserious consequences if misrecognized, the likelihood produced by thelanguage model may be higher, which would trigger an alert regarding therecognition results such that a reviewer could review the result morecarefully and thereby prevent the serious consequences.

Using any of these evaluation techniques alone or together, or using anyother suitable evaluation technique, when an evaluation of therecognition results indicates there is a potential error in the toprecognition result that changes a meaning of the result in a way that issignificant for the domain, the potential significant error may beidentified to a reviewer during a review process so that the potentialsignificant error can be reviewed carefully. For example, an alert couldbe triggered regarding the results in any suitable way, examples ofwhich are described below. For example, when an indication of apotential significant error is detected, an alert may be triggered tonotify a reviewer that a significant error might be present in therecognition result of the ASR system. When the reviewer is notifiedabout a potential significant error, the reviewer may be more likely toclosely review and, if desired, correct the potential significant errorand not inadvertently overlook the error.

The three examples of evaluation techniques given above may be usedtogether or separately to identify potential significant errors inrecognition results that are a consequence of a misrecognition of speechinput by an ASR system. Applicants have recognized and appreciated thatthe third example of an evaluation technique may also be used to detecterrors that do not stem from misrecognition by an ASR system, butinstead stem from mistakes made by a person providing the speech input,such as mistakes in word choice. The third exemplary evaluationtechnique was described above as comparing recognition results to alanguage model to identify when a recognition result is similar to aphrase that is sufficiently common in the language that there is achance that the recognition result is incorrect and that the speechinput instead included the similar phrase. This technique may also beable to recognize potential errors even when the recognition result iscorrect for the speech input, but wherein the speech input wasincorrect. When a correct recognition result for an erroneous speechinput is similar to another phrase in the language (which may be thephrase the speaker intended to speak) and the other phrase issufficiently common in the language, this may trigger an alert by theevaluation engine as a potential significant error in the same manner asif the recognition result were incorrect. A reviewer may then be able toreview the recognition result and the indication of the potentialsignificant error alongside other potential significant errors. Thereviewer may be able to identify from the context of the recognitionresult (e.g., from other speech input received together in time with thespeech input and relating to the same document or topic) that thespeaker misspoke and that recognition result should be corrected toinclude the similar word/phrase identified by the language model oranother word/phrase.

Applicants have additionally recognized and appreciated that, in somecases, erroneous words/phrases that may appear in a recognition resultof an ASR system may be words or phrases that are not common in thedomain to which the speech input relates.

A domain may be a field of use for the ASR system (e.g., a medical orclinical environment which may be general medicine or limited to aparticular medical specialty, a particular business environment, or anyother type of domain). A domain may have words that are often used inspeech or text related to the domain and also words that are seldom usedin speech or text related to the domain. Applicants have recognized andappreciated that when a word that appears in a recognition result isuncommon in the domain, this may be indicative of a potential error inthe recognition result. For example, when a recognition result relatedto the medical domain includes a word that is not common in medicine(e.g., “pizza” in the expression “there is evidence of active pizza inthe abdomen”), this may be indicative of a potential error in therecognition result. A recognition result may be evaluated based on anysuitable domain or domains that are general or specific, as embodimentsare not limited in this respect. For example, “medical examination” maybe a domain for which recognition results are evaluated in someembodiments, and “medical examination of a female” may be a domain forwhich recognition results are evaluated in other embodiments. When arecognition result is evaluated for the “medical examination of afemale” domain, a word that is common in the medical context butuncommon for females (e.g., prostate) may be identified as potentiallyerroneous when included in a recognition result.

Accordingly, in some embodiments a recognition result of an ASR systemmay be evaluated to determine whether the recognition result includes aword or phrase that is unlikely to occur in the domain of the speechinput on which the recognition result is based. When the recognitionresult includes an unlikely word or phrase, any suitable action may betaken. For example, when an indication of a potential error is detected,an alert may be triggered to notify a reviewer that an error may bepresent in the recognition result of the ASR system. When the revieweris notified about a potential error, the reviewer may be more likely toclosely review and, if desired, correct the error and not inadvertentlyoverlook the error. Determining whether a recognition result includes anunlikely word or phrase may be carried out in any suitable manner. Forexample, a set of unlikely words/phrases may be maintained for a domainand the words/phrases in the recognition result may be compared to theset to determine whether any of the words/phrases are unlikely in thedomain and indicative of potential errors. As another example, alanguage model for the domain may be maintained that includes, for wordsand phrases, a value indicating a likelihood of the words or phrasesappearing in speech or text related to the domain. In some embodiments,when the value of the domain language model for a word or phrase of arecognition result is below a threshold, the word or phrase may beidentified as being unlikely to appear in the domain and indicative of apotential error. The domain language model may be the same languagemodel as is used by the ASR system in generating speech recognitionresults or may be a different language model that is used in evaluatingthe recognition results of the ASR system.

Embodiments may evaluate recognition results that are formatted in anysuitable manner, as embodiments are not limited in this respect. SomeASR systems may produce as output from a speech recognition processresults formatted as an “N best” list of recognition results (N beingtwo or more) that includes a top recognition result and N−1 alternativerecognition results. In embodiments that operate with ASR systems thatproduce recognition results in an N best list format, the list of N bestrecognition results may be evaluated to determine whether the toprecognition result includes any indications of potential significanterrors. Other ASR systems may produce as output of a speech recognitionprocess a “lattice” of words that identifies strings of words thatpotentially correspond to the speech input. The lattice format mayinclude multiple nodes, where each node in the lattice corresponds to aword that potentially corresponds to sounds in the speech input, andwhere the nodes are interconnected in a directed manner that createspaths through the nodes, each path identifying a string of words that isa possible recognition result that corresponds to the speech input. Incases where the recognition results represented by a lattice all includethe same word corresponding to the same sounds of the speech input, thepaths through the lattice will all pass through the node correspondingto that word. In cases where the recognition results represented by alattice include different words corresponding to the same sounds of thespeech input, the paths through the lattice will pass through differentnodes corresponding to the different words at the point in therecognition results where the results differ as to which wordcorresponds to the same sounds of the speech input. In embodiments thatoperate with ASR systems that produce recognition results in a latticeformat, an evaluation of the recognition results may be performeddirectly on the lattice or may be carried out on an N best list ofrecognition results produced by processing the paths through the latticeto create each of the recognition results represented by the lattice.Other ASR systems may produce recognition results in other formats, andrecognition results may be processed in these other formats, asembodiments are not limited to processing recognition results in anyparticular format. Accordingly, while in various illustrativeembodiments described below recognition results are described as beingin an N best list format, it should be appreciated that this is notillustrative and that embodiments are not limited to operating withresults in an N best list format.

Described below are various examples of ways in which the aspects of thepresent invention described herein may be implemented. However, itshould be appreciated that aspects of the invention described herein arenot limited to the illustrative examples discussed below, as they may beimplemented in any suitable manner.

FIG. 1 illustrates one embodiment of a speech processing system 100 thatmay implement some aspects of the invention described herein. The speechprocessing system 100 may include many different components, some ofwhich are shown in FIG. 1.

The speech processing system 100 of FIG. 1 includes an automatic speechrecognition (ASR) system 102 to carry out a speech recognition processon speech data 104A received from the data storage 104 to determine oneor more recognition results for the speech data. After the recognitionresults are produced by the ASR system 102, the recognition results maybe analyzed by an evaluation engine 106 to determine whether therecognition results include any indications of potential errors. FIG. 1illustrates the ASR system 102 directly communicating with theevaluation engine 106, but in some embodiments the ASR system 102 mayoutput the recognition results to another component (e.g., the datastorage 104) and the evaluation engine 106 may receive or retrieve theresults from the other component. The evaluation engine 106 may use aset of confusable words and/or phrases 106A, a set of unlikely words106B, a language model 106C, a semantic interpretation engine 106D,and/or prosody and/or hesitation vocalization information 106E (alldescribed in detail below) in performing the evaluation, or may use anycombination of two or more of these. Following evaluation by theevaluation engine 106, the recognition results, along with anyinformation created by the evaluation engine 106 regarding the results,may be stored in the data storage 104 as information 104B regarding therecognition results. The information 104B may then be used in anysuitable manner, examples of which are discussed below. For example, theinformation may be used to display one or more of the recognitionresults to a reviewer during a review process that identifies potentialsignificant errors in the results that were identified by the evaluationengine 106.

The components of the system 100 may be implemented in any suitablemanner to carry out the techniques described herein. For example, thecomponents of the speech processing system 100 may, in some embodiments,be implemented on one computing device, which may be any suitablecomputing device, as embodiments are not limited to operating with anyparticular computing devices. For example, the computing device may be alaptop or desktop personal computer, a personal digital assistant (PDA)or mobile phone, a server or any other computing device. In otherembodiments, the components of speech processing system 100 may bedistributed among any number of multiple computing devices that may beinterconnected in any suitable manner. For example, some functionalityof the ASR system 102 may be implemented on a user-interactive computingdevice and other functionality of the ASR system 102 may be implementedon another computing device (e.g., a server) remote from theuser-interactive device. Some interconnections between computing devicesmay include a wired or wireless computer communication network such as alocal area network, an enterprise network, or the Internet. When thecomponents are implemented on multiple computing devices, the componentsmay be distributed between computing devices in any suitable manner, onenon-limiting example of which includes client-server models that placesome components on user-interactive devices and other components onservers located remotely from the user-interactive devices.

The ASR system 102 may be one or more systems that apply any suitableASR technique(s) to speech input to perform a speech recognition on thespeech input, as embodiments are not limited to implementing anyparticular ASR techniques. In the embodiment of FIG. 1, the ASR system102 processes speech input using one or more acoustic models 102A andone or more language models 102B that the ASR system 102 uses in aprobabilistic process to determine one or more recognition results thatmay correspond to the speech input. Each of the recognition resultsproduced by the ASR system 102 using the acoustic models 102A andlanguage models 102B may be associated with a confidence valueindicating a confidence of the ASR system 102 that the result iscorrect. The confidence value may be derived from probabilitiesdetermined through the application of the models 102A, 102B to thespeech input during the speech recognition process. For example, in someprobabilistic processes, the acoustic model(s) 102A may be used toidentify a probability that a sound used in the speech input is aparticular phone or phoneme of a language and to identify potentialstrings of words that correspond to the phones/phonemes, and thelanguage model(s) 102B may be used to determine, based on how words orphrases are commonly used and arranged in the language in general, or ina particular domain, a probability that each of the strings of wordsmight correspond to the speech input and thereby identify the mostlikely strings.

Through applying the models 102A, 102B, an ASR system 102 thatimplements such a probabilistic process can determine a result of aspeech recognition process that is a string of one or more words thatmight correspond to the speech input. The ASR system 102 also produces,for the result, a confidence of the ASR system 102 (which may berepresented as a probability on a scale of 0 to 1, as a percentage from0 percent to 100 percent, or in any other way) that the result is acorrect representation of the speech input. In performing such aprobabilistic process, an ASR system 102 may yield multiple recognitionresults, each of which includes a string of words and a confidencevalue. When an ASR system 102 produces multiple recognition results, theASR system 102 might order and filter the results in some way so as tooutput N results as the results of the speech recognition process, whereN is an integer of two or more. For example, the ASR system 102 mightorder the recognition results according to the confidence of the ASRsystem 102 that a result is a correct representation of the content ofthe speech input and then select at most N results from the orderedrecognition results. The N best results produced in this way mayformatted as an “N best” list of recognition results, as a lattice ofrecognition results, or in any other suitable manner. The recognitionresults, however formatted, may include or represent a “top result” or“most likely result,” which the ASR system 102 has identified as beingmost likely to be a correct representation of the content of the speechinput, and N−1 alternative recognition results that the ASR system hasidentified as the results that are next most likely, after the topresult, to be a correct representation of the speech input.

While the operations of some types of probabilistic speech recognitionprocesses have been described, it should be appreciated that embodimentsare not limited to applying any particular type or types of speechrecognition processes.

To perform a speech recognition process on a speech input, the ASRsystem 102 may receive data regarding the speech input in any suitablemanner. In the example of FIG. 1, the ASR system 102 receives dataregarding a speech input from a data storage 104. The speech data 104Areceived by the ASR system 102 may be any suitable data regarding speechinput that may be used in a speech recognition process to producerecognition results corresponding to the speech input. For example, thespeech data 104A may include or be derived from audio data that wasreceived via audio capture hardware, such as a microphone, and/or thatwas processed following capture in any suitable manner, such as througha filtering process, as embodiments are not limited in this respect. Thespeech data 104A may include, for example, audio data for the speechinput that is stored in an audio format. Additionally or alternatively,the speech data 104A may include data derived from an analysis of theaudio data, such as data regarding an audio spectrum of the speech inputor any other data.

The data storage 104 that stores the speech data 104A may be anysuitable storage medium or collection of media (within one device, ordistributed between multiple devices) that a computing device is able toread from and write to, as embodiments are not limited in this respect.Illustrative examples of storage media include random access memories(RAM), hard drives, optical disks, and virtual disks, although any othertype of storage medium may be used.

It should be appreciated that while FIG. 1 illustrates the ASR system102 processing stored speech data from the data storage 104, embodimentsare not limited to processing stored speech data or speech data from anyparticular source. In some embodiments, for example, an ASR system mayprocess speech data received directly from a microphone.

After the ASR system 102 carries out a speech recognition process on thespeech data 104A and produces one or more recognition results, therecognition results may be provided to the evaluation engine 106. Theevaluation engine 106 determines whether the recognition results includeany indication(s) of one or more potential errors in the recognitionresults that may be semantically significant. In one embodiment, todetermine whether the recognition results include indications of suchpotential errors, the evaluation engine 106 may evaluate the recognitionresults using at least one criterion that was not used by the ASR system102 to determine the recognition results, although not all embodimentsare limited in this respect. As discussed above, the ASR system 102 mayuse various criteria to determine multiple recognition results, orderthe recognition results according to a confidence of the ASR system 102that each result is correct, and filter the recognition results todetermine the N best recognition results. The evaluation engine 106 mayevaluate the recognition results output by the ASR system 102 accordingto any one or more criteria, and in one embodiment the criteria includeat least one criterion that was not used by the ASR system 102 indetermining the N best recognition results. The evaluation engine mayuse the one or more criteria to evaluate recognition results output bythe ASR system 102 without identifying a new order of the recognitionresults.

The evaluation engine may operate according to any of the evaluationtechniques described herein to determine whether the recognition resultscontain any indications of potential significant errors in therecognition results. For example, as discussed above, in someembodiments the evaluation engine 106 may compare the top result of therecognition process to one or more alternative recognition resultsincluded in the N best results output by the ASR system 102. Variousexamples of evaluations that may be carried out by the evaluation engine106 are discussed below in connection with FIGS. 2-9, though it shouldbe appreciated that the aspects of the present invention relating toperforming an evaluation of the speech recognition results are notlimited to implementing these exemplary processes.

As mentioned above, in some embodiments, the evaluation engine 106 mayuse a set of confusable words 106A, a set of unlikely words 106B, alanguage model 106C, a semantic interpreter 106D, prosody and/orhesitation vocalization information 106E, or any combination of two ormore of these in evaluating the recognition results. The semanticinterpreter 106D may analyze recognition results provided by theevaluation engine 106 to determine meanings of the recognition results.The semantic interpretation engine 106D may be implemented in anysuitable manner and may carry out any suitable semantic interpretationprocess. For example, some embodiments may implement a semanticinterpretation process like the one described in U.S. Pat. No.7,493,253, titled “Conceptual world representation natural languageunderstanding system and method” and dated Feb. 17, 2009. As anotherexample, some embodiments may implement a semantic interpretationprocess like the one described in “A Statistical Model for MultilingualEntity Detection and Tracking” by R. Florian et al., published in theproceedings of the 2004 Human Language Technologies and North AmericanAssociation for Computational Linguistics Conference. It should beappreciated, however, that the aspects of the present invention thatrelate to employing a semantic interpreter are not limited to oneimplemented in either of these manners, as any suitable semanticinterpretation process may be used.

The evaluation engine 106, upon evaluating the multiple recognitionresults produced by ASR system 102, may produce any suitable informationwhen an indication of a potential significant error is identified in therecognition results. For example, the information produced by theevaluation engine 106 may trigger an alert indicating that an errormight be present in the recognition results and the information mayoptionally also identify the potential error. In some embodiments, theinformation identifying that an error might be present may includeinformation identifying a recognition result and/or a particularposition, word, or phrase of a recognition result from which thepotential error was identified. The evaluation engine 106 may alsocreate information regarding the potential error identified by theevaluation engine 106, such as information identifying a difference infacts between recognition results that the evaluation engine 106identified as indicative of a potential error.

Following the evaluation by the evaluation engine 106, one or more ofthe recognition results (e.g., the top result and optionally one or moreof the alternative recognition results) may be output by the evaluationengine 106 and optionally stored (e.g., in the data storage 104) alongwith information 104B regarding the recognition results. In someembodiments, information created by the evaluation engine 106 alsoincludes information identifying a potential error and optionally apotential correction.

The information 104B may be used in any suitable manner. In someembodiments, for example, the information 104B may be used in a reviewprocess by a reviewer (e.g., a human reviewer) that reviews therecognition results produced by speech processing system 100. The reviewprocess may be carried out for a single speech input at a time or formultiple speech inputs together. When multiple speech inputs areprocessed together, they may be grouped at any suitable way (e.g., theymay include all speech inputs received together in a period of time,such as all speech inputs received during one dictation session, mayrelate to a single document or subject or by any other suitable way).During the review process, the top result of the multiple recognitionresults may be presented to the reviewer. The information created by theevaluation engine 106 during the evaluation of the recognition result(s)produced by ASR system 102 may be used to call the reviewer's attentionto the presence of potential significant errors in the top result in anysuitable way (e.g., as by annotating the top result in some manner).Illustrative review processes and ways of calling the reviewer'sattention to potential significant errors are discussed in detail below.Regardless of how the review process is carried out, when the revieweris alerted to the presence of potential significant errors in therecognition result, the reviewer may be more likely to correct an errorand thereby prevent any serious consequences that might have resultedfrom the inclusion of the potential significant error in the recognitionresult (e.g., consequences of the result being incorrect in a way thatis semantically meaningful for the domain).

As discussed above, an evaluation engine (which may be part of a speechprocessing system) may carry out any suitable evaluation process todetermine whether recognition results include potential significanterrors. FIG. 2 illustrates one non-limiting process that may be used insome embodiments by an evaluation engine to make such a determination.

Prior to the start of the process 200 of FIG. 2, an ASR system carriesout a speech recognition process on speech input and produces the N bestrecognition results. The N best recognition results include a top resultand one or more alternative recognition results, along with anindication (e.g., a probability) for each that indicates a confidence ofthe ASR system that the result is correct. The process 200 begins inblock 202, in which the evaluation engine receives the N best resultsproduced by the ASR system.

In block 204, the evaluation engine reviews the N best results andcompares the top result to the alternative recognition results todetermine whether there are one or more discrepancies between the topresult and any of the alternative recognition results that may beindicative of a potential error in the top result that is semanticallymeaningful in the domain. The review and comparison of block 204 may becarried out in any suitable manner, examples of which are discussedbelow in connection with FIGS. 3-6. For example, in some embodiments,facts determined from a semantic interpretation of the top result may becompared to facts determined from a semantic interpretation of thealternative recognition results to determine whether there are anydifferences in the facts that may be indicative of a potential errorthat is semantically meaningful in the domain. As another example, insome embodiments words included in the top result may be compared towords included in the alternative recognition result to determinewhether there is a difference in the words that may be indicative of apotential error that would be semantically meaningful in the domain.

In some embodiments, one or more thresholds may be evaluated as part ofthe review and comparison of block 204. For example, confidences inrecognition results or likelihoods determined by an ASR system usingacoustic and/or language models may be compared to one or morethresholds to determine whether the confidence, likelihood, etc. isabove or below the threshold. Actions may then be taken based on thecomparison to the threshold, such as in the case that the evaluationengine determines whether the confidence, likelihood, etc. issufficiently high or low (as compared to the threshold) for the actionto be taken. In some embodiments in which an evaluation engine uses oneor more thresholds as part of a review and comparison of recognitionresults, any suitable fixed or variable thresholds set to any suitablevalue(s) may be used, as embodiments are not limited to using anyparticular values for thresholds. Embodiments are not, for example,limited to using thresholds of 50% or any other number that would beused to determine whether a likelihood of something occurring indicatesthat thing is more likely than not to occur. It should also beappreciated that embodiments are not limited to using thresholds, andthat some embodiments may perform the review and comparison of block 204without evaluating thresholds.

Following the review and comparison of block 204, in block 206 theevaluation engine determines whether the comparison identified anyindications of potential semantically meaningful errors in therecognition results. If so, then in block 208 the evaluation enginetriggers an alert regarding the top result in any suitable way. Forexample, the alert may be triggered in block 208 by storing informationindicating that a potential significant error has been detected, whichmay cause an alert to be raised when a review process is carried out forthe recognition results. Alternatively, in some embodiments, triggeringan alert in block 208 may include directly raising an alert such as byinstructing that the alert be raised. An alert may be raised in anysuitable way, including by presenting a visual and/or audible messagevia a user interface through which the recognition results are to bepresented for review.

In addition to triggering an alert in block 208, when the evaluationengine determines in block 206 that the recognition results include anindication of a potential semantically meaningful error, in block 210the evaluation engine may optionally store information regarding thepotential error that may be semantically meaningful. The informationidentifying the potential significant error may include any suitableinformation. For example, storing the information identifying thealternative recognition results in block 210 may include identifyingwhich of the alternative results prompted the identification of thepotential significant error, a word or words included in the resultsthat prompted the identification of the potential significant error, aposition in a result based on which the potential significant error wasidentified, any combination of the foregoing information, and/or anyother suitable information. It should be appreciated that in the process200 and in other processes below that include storing information aboutthe error, the act of storing information regarding the potentialsignificant error is optional. In some embodiments, an alert may betriggered for the reviewer (as in block 208) without information beingstored (as in block 210). Whether information on the error is stored oran alert is generated directly, in some embodiments, the reviewer may beinstructed, as a result of the triggering of the alert, to review thetop recognition result carefully due to the potential error, but may notbe presented with information about the alternative recognitionresult(s) that aided in identifying the potential error.

After an alert has been triggered in block 208 and information regardingthe error has been optionally stored in block 210, or if no potentialerrors were identified in block 206, the process 200 ends. Following theprocess 200, any suitable actions may be taken. Optionally, a reviewprocess may be carried out for the top recognition result. In such areview process, as a result of the alert triggered in block 208, anotification may be presented to a reviewer (e.g., a visual notificationpresented to a human reviewer or other type of notification) that thetop recognition result may include an error that changes the meaning ofthe result in a way that is significant in the domain to which therecognition result relates. During the review process, information aboutthe potential significant error may be presented to the reviewer basedon the information stored in block 210. Examples of ways in which areview process can be carried out are discussed below in connection withFIG. 10.

The process 200 of FIG. 2 was described as comparing the top recognitionresult to all of the N−1 alternative recognition results of therecognition results to determine whether there is an indication of apotential significant error in the top result. It should be appreciated,however, that embodiments are not limited to comparing the toprecognition result to all of the alternative recognition results of theN best recognition results. Rather, some embodiments (includingembodiments that carry out the process of FIG. 2 or the processes ofFIGS. 3-6, or similar processes) may compare the top recognition resultto only some of the alternative recognition results of the N bestrecognition results and may not evaluate others of the alternativerecognition results. Embodiments that evaluate only some of thealternative recognition results may identify the alternative recognitionresults to be evaluated in any suitable manner, as embodiments are notlimited in this respect. Some embodiments, for example, may evaluateonly a fixed number of alternative recognition results, where the fixednumber is less than the number N−1 of alternative recognition results(e.g., evaluate three alterative results where the N best recognitionresults includes 10 alternative results). As another example, otherembodiments may review confidence values provided by the ASR system forindividual alternative recognition results to determine whether toevaluate an alternative recognition result. For example, an evaluationengine may compare the confidence value of the ASR system for analternative recognition result to a threshold and evaluate thealternative recognition result only when the confidence is above thethreshold. This threshold may be different (e.g., higher) than athreshold used by the ASR system in identifying the alternativerecognition results to be included in the N best recognition results. Asanother example of a way in which the evaluation engine may reviewconfidence values, the evaluation engine may compare confidence valueswithin the alternative recognition results to determine whether theconfidence for one or more of the alternative recognition results ismuch lower than the confidence value for the top recognition result orothers of the alternative recognition results. A wide spread ofconfidence values may indicate that the ASR system has identified someof the alternative recognition results as being much less likely to becorrect recognitions for speech input than other results. When there isa difference between confidence scores that exceeds a thresholddifference, an evaluation engine may refrain from evaluating alternativerecognition results that are much less likely to be correct byevaluating only the recognition results having the higher confidencescores separated from the other results by at least the thresholddifference.

It should be appreciated that embodiments that evaluate only some of thealternative recognition results may use any suitable criterion orcriteria in determining which alternative recognition results toevaluate. Embodiments are not limited to using any of the exemplarytechniques described above or any other specific techniques to selectresults to evaluate.

As mentioned above, an evaluation engine may act in any suitable mannerto review and compare recognition results. FIGS. 3-6 illustrateillustrative techniques that may be implemented by embodiments to reviewand evaluate recognition results. It should be appreciated, however,that the illustrative techniques described below are merely examples andthat embodiments of the invention that relate to evaluating recognitionresults are not limited to implementing any of these techniques.

FIG. 3 illustrates a process that applies a semantic interpretation toreview and compare recognition results to determine whether therecognition results include one or more indications of potential errorsthat are semantically significant in the domain. In particular, theprocess 300 of FIG. 3 determines, from a semantic interpretation of therecognition results, facts that are expressed in each of the recognitionresults and compares the facts from the top recognition result withthose from other recognition results to identify whether they differ ina way that is significant for the domain. When the facts differ in a waythat is significant for the domain, this may indicate that there is apotential error in the top recognition result that is significant forthe domain.

As discussed above, embodiments may operate with ASR systems thatproduce speech recognition results in an N best list format, a latticeformat, or any other suitable format. In embodiments that operate withrecognition results in an N best list format, the process 300 of FIG. 3may operate by semantically interpreting each of the recognition resultsin the list of N best recognition results. In some embodiment thatoperate with recognition results in a lattice format, an evaluationengine may first produce an N best list of recognition results byprocessing the lattice and the paths through the lattice to identifyeach of the N recognition results represented by the lattice, includinga top recognition result that the lattice indicates that ASR system wasmost confident is a correct representation of the speech input and N−1recognition results that the ASR system was less confident are correctrepresentations of the speech input.

The process 300 begins in block 302, in which the evaluation enginereceives the N best recognition results of a recognition processperformed by an ASR system. As mentioned above, the N best recognitionresults include both a top recognition result and N−1 alternativerecognition results. In some ASR systems, each of the recognitionresults is associated with a likelihood that the recognition result is acorrect representation of the speech input processed by the ASR system,but the process of FIG. 3 is not limited to use with such an ASR system.

In block 304, the evaluation engine provides the top recognition resultto a semantic interpretation engine to semantically interpret the topresult. The semantic interpretation of block 304 identifies factsexpressed in the top recognition result based on analyzing the content(e.g., words and phrases) of the recognition result and a domain towhich the recognition result relates. Any suitable semanticinterpretation process may be used by a semantic interpretation engine,including a generic semantic interpretation process that identifiesfacts expressed in a result in a manner independent of any particulardomain, or a domain-specific semantic interpretation process thatidentifies facts relating to a particular domain. For example, in someembodiments a clinical language understanding (CLU) semanticinterpretation engine may be used that is specifically designed toidentify facts relating to a clinical encounter with a medical patient.Other semantic interpretation engines operating in other domains may beused in other embodiments. In some other embodiments, a semanticinterpretation engine that interprets facts according to multipledifferent specific domains may be used, or multiple different semanticinterpretation engines that each interpret facts according to a specificdomain may be used together. As was mentioned above in connection withFIG. 1, any suitable semantic interpretation process may be used, as theembodiments of the invention that employ a semantic interpretationprocess are not limited in this respect.

After the top recognition result has been semantically interpreted inblock 304, the evaluation engine begins a loop in which the evaluationengine semantically interprets each of the N−1 alternative recognitionresults using the same semantic interpretation engine (or engines) thatwas used in block 304 to interpret the top result. In block 306, theevaluation engine selects the next alternative recognition result thathas not yet been interpreted. In block 308, the alternative recognitionresult selected in block 304 is semantically interpreted to determinefacts expressed in the alternative result. The evaluation engine thencompares, in block 310, the facts identified in the top result to thefacts that were identified from the recognition result interpreted inblock 308 to determine whether they differ in a way that is semanticallymeaningful. For example, if the speech input related to a clinicalencounter, the semantic interpretation of the top result yields one ormore facts that “the growth is malignant,” and the semanticinterpretation of one of the alternative recognition results yields oneor more facts that “the growth is nonmalignant,” the difference between“malignant” and “nonmalignant” in the facts may be indicative of apotential error in the top recognition result that is semanticallymeaningful, the existence of which should be identified to a reviewer.

The determination in block 310 may be made in any suitable manner. Forexample, in some embodiments, the evaluation engine may determinewhether there is any difference between the facts identified in thealternative recognition result and the top result, without furtherevaluating those differences and implicitly identifying any differencesin facts as generally meaningful. A difference in facts may result fromthe alternative result including one or more facts not included in thetop result, or from the alternative result not including one or morefacts included in the top result.

If the evaluation engine were to interpret any difference in facts assemantically meaningful, in some environments this may result in a largenumber of “false alerts” where potential errors are flagged that wouldnot be semantically meaningful, which could reduce the usefulness of theevaluation because the reviewer may begin to discredit or not paysufficient attention to the alerts. Therefore, in some embodiments, thedifference(s) in the facts are evaluated further to determine whetherthey may be semantically meaningful, which may limit the types ofdifferences that generate an alert.

In some embodiments, for example, the evaluation engine may determinewhether there is a difference in “significant” facts between thealternative result and the top result. Whether there is a difference insignificant facts may be determined in some embodiments by marking assignificant some of the types of facts the semantic interpretationengine is configured to find in a recognition result. The semanticinterpretation engine may be configured to interpret multiple differenttypes of facts that may be expressed in a recognition result and mayevaluate a recognition result to identify one or more facts that eachcorresponds to a fact type. A type of fact may be a semantic category orclassification of a piece of information that can be expressed in arecognition result and that the semantic interpretation engine may beconfigured to extract from a recognition result. In some domains, someof the facts may be more meaningful than others, and thus some facts maybe important or significant in the domain. In the medical domain, forexample, facts relating to symptoms, diagnoses, and treatments may besignificant. Therefore, in some embodiments types of facts that thesemantic interpretation engine may be configured to extract fromrecognition results that correspond to symptoms, diagnoses, andtreatments may be marked as significant. When some types of facts areidentified as significant, the evaluation engine may determine whetherthere is any difference in significant facts (i.e., facts having a facttype marked as significant) identified in recognition results. Adifference in significant facts may be detected when an alternativerecognition result includes a significant fact not in the top result orwhen an alternative recognition results is missing a significant fact inthe top result. For example, in the medical context, when a semanticinterpretation of the top recognition result indicates that at least apart of the result relates to the diagnosis of “pneumonia” and asemantic interpretation of an alternative recognition result insteadindicates that at least a part of the result instead relates to thediagnosis “acute pneumonia,” the difference in diagnoses may be treatedas a difference in significant facts.

It should be appreciated that embodiments are not limited to determiningin any particular way(s) whether there is a difference in facts in the Nbest results, or whether any such difference is significant.Accordingly, while several examples have been given, still otherprocesses may be used in block 310 to identify whether facts aredifferent and optionally whether any such difference is significant, asembodiments are not limited in this respect.

If the evaluation engine determines in block 310 that there is asignificant difference in facts between the top result and thealternative recognition result selected in block 306, then in block 312the evaluation engine triggers an alert regarding a potential error inthe top results, and in block 314 optionally stores informationidentifying the alternative recognition result that resulted in thetriggering of the alert. The triggering and storing of blocks 312, 314may be carried out in any suitable manner, including using any of thetechniques described above in connection with blocks 208 and 210 in FIG.2.

After the triggering and storing in blocks 312, 314, or if nosignificant difference in facts was determined in block 310, the processdetermines in block 316 whether there are any alternative recognitionresults that have not yet been semantically analyzed and compared to thetop result. If so, the process returns to block 306 to select andevaluate another recognition result. If, however, the evaluation enginedetermines in block 316 that all alternative recognition results havebeen interpreted and compared to the top result, then the process 300ends.

In some embodiments, a semantic interpretation process may be resourceintensive, such as by requiring a relatively large amount of time,processor resources, and/or storage resources to complete. In some suchembodiments, an evaluation engine may limit the number of recognitionresults that are semantically interpreted by the process 300 of FIG. 3.For example, the evaluation engine may compare a confidence of the ASRsystem that a result is a correct recognition of the speech input to athreshold and, if the confidence is below the threshold, refrain fromsemantically interpreting the result. It should be appreciated that allembodiments (as some embodiments interpret all of the N best resultsoutput by the ASR) are not limited to determining whether tosemantically interpret recognition results or, when such a determinationis to be made, to making the determination in any particular manner.

While the example of FIG. 3 identifies potential significant errors byanalyzing differences in facts extracted from the top and alternativerecognition results, some embodiments may not use semanticinterpretation and may not compare facts expressed by the recognitionresults. Instead, some embodiments may directly analyze differences inwords and/or phrases included in the top recognition result andalternative recognition results to determine whether any differences inthe words/phrases are indicative of a potential significant error in therecognition results. FIGS. 4-6 illustrate examples of techniques thatexamine recognition results for differences in words and/or phrases thatmay be indicative of a potential significant error in the recognitionresults.

In embodiments that directly examine words/phrases of recognitionresults, an evaluation engine may determine whether a potentialsignificant error is present in recognition results by examining one ormore sets of words/phrases. The sets of words/phrases may includewords/phrases that are acoustically similar and subject to beingconfused by an ASR system performing a speech recognition process on aspeech input. Each set (e.g., a pair) of acoustically similarwords/phrases may include words/phrases that include similar sounds orthat differ from one another by only one or a few sounds. For example,in a medical context, the words “arterial” and “arteriosclerotic” may beconfusable due to the similarity of the sounds in the first parts of thetwo words. Another example is “malignant” and “nonmalignant” due to theoverlap of many of the sounds used in the two words. Further, a set ofwords/phrases may include words/phrases that, when confused by an ASRsystem, would change a meaning of a recognition result in a way thatwould be semantically significant (e.g., significant in a domain). Forexample, while the words “to” and “too” may be acoustically similar andconfusable by an ASR system and an incorrect substitution of one for theother would be erroneous, the error may not have serious consequences insome domains if not corrected, so that these words would not beconsidered significant in these domains. Conversely, as discussed above,in the medical context if the word “malignant” is erroneouslysubstituted for the word “nonmalignant” or vice versa, a patient may beimproperly treated, which may have serious consequences for the patientand the medical institution. By limiting the list of pairs of words toonly words that, when misrecognized, would result in seriousconsequences, the system may limit the number of cases in which an alertis raised and thereby focus a reviewer's attention on the errors thatare most significant when present. It should be appreciated, however,that embodiments are not limited to evaluating only significant words orwords associated with serious consequences, as the techniques describedherein may be used to evaluate any word(s).

In some embodiments, a set of words/phrases may include a “null” word orphrase representing an absence of any word or phrase. This may be donebecause a short word/phrase may be acoustically similar to no sound (orno word/phrase) at all, as the short word/phrase may include only asmall portion of sound, or because a short word/phrase may beacoustically similar to the other words around the short word/phrasesuch that an ASR system may misrecognize the short word/phrase as merelya portion of the other words. For example, a word like “no” that hasonly a very short sound may be acoustically confused with silence (i.e.,a “null” word) and thus a speech recognition result could incorrectlyinclude the silence (i.e., no word or phrase) in place of the word “no.”As another example, the word “no” in the phrase “no pneumonia was found”may be acoustically similar to the sound of the “pneu” in the word“pneumonia” and the ASR system may incorrectly identify the soundscorresponding to the word “no” as merely a part of the sounds for theword “pneumonia” and thus a speech recognition result would incorrectlynot include a word or phrase corresponding to the word “no” in thespeech input. The inclusion of the null word instead of the actualword/phrase can be significant as it may result in the recognitionresult having an opposite or otherwise different meaning. For example,when processing the speech inputs “there is no evidence of activedisease” or “no pneumonia was found,” an ASR system may substitute anull word for the word “no” (i.e., may fail to recognize that “no” wassaid), resulting in the incorrect “there is evidence of active disease”or “pneumonia was found,” either of which could have significantconsequences as they could lead to a patient being incorrectly treated.

Thus, two or more words/phrases that are acoustically similar and that,when misrecognized for one another would have a significantconsequences, can be included in a set of words/phrases. As set outbelow, in some embodiments an evaluation engine may use thewords/phrases of a set to review recognition results and determinewhether there are any indications of potential significant errors in therecognition results. Determining whether there are any indications ofpotential significant errors may include determining whether there is anindication in the recognition results that a word/phrase of a set wasincluded in the top recognition result in error.

In some embodiments, sets of words/phrases (which may be pairs ofwords/phrases) to be evaluated by an evaluation engine may form an“alert” list against which the recognition results are evaluated in anyof various ways to determine whether to generate an alert regardingpotential significant errors in the recognition results. In someembodiments, as described below in connection with FIG. 4, theevaluation engine may determine, when the top result includes one wordof a word pair, whether one or more of the alternative results includesthe other word of the word pair and trigger an alert if so. As mentionedabove, in some embodiments a word pair may include a null word as oneword of the pair and this may present a special case of such anevaluation. As a null word appears in multiple positions throughout eachof the recognition results (e.g., at the beginning and end of eachresult, and between each word of each result), the evaluation engine maysimply evaluate the recognition results to determine whether the other,non-null word of the word pair appears in any of the recognitionresults, without evaluating words of the recognition results todetermine if the null word appears. If the other, non-null word appears,the evaluation engine may trigger an alert.

As should be appreciated from the discussion of speech recognitionprocesses above, words/phrases may appear in the recognition resultsbased on an analysis of speech input by an ASR system using one or moreacoustic models and one or more language models, and a resultingprobability that a segment of the speech input corresponds to theword/phrase. In many cases, when a word/phrase appears in therecognition results and is evaluated using the evaluation engine, boththe acoustic model(s) and the language model(s) indicate a highprobability that a segment of the speech input corresponds to theword/phrase. However, all embodiments are not limited to evaluatingwhether a word or phrase indicates the presence of a potentialsignificant error when both the acoustic model(s) and the languagemodel(s) indicate a high probability that the word/phrase appeared inthe speech input. Rather, for reasons discussed below, in someembodiments, an evaluation engine may determine whether a recognitionresult includes a potential significant error based on a probabilityprovided by the acoustic model(s) and not the language model(s), orbased on a probability provided by the language model(s) and not theacoustic model(s).

Accordingly, in addition to or as an alternative to comparingwords/phrases included in a top result to words/phrases included in thealternative recognition results produced by the ASR system, in someembodiments an evaluation engine may evaluate recognition results usingan output directly from an acoustic model and/or a language model todetermine whether there is a potential significant error in the toprecognition result.

For example, in some embodiments the evaluation engine may determine,when one word of a word pair in an alert list appears in a top result,whether an acoustic model indicates a likelihood above a threshold thatthe other word of the word pair may have been used in the speech inputrather than the word that appeared in the top result and thus alikelihood that a correct recognition of the speech input includes theother word of the pair. To do so, the evaluation engine may determine,using the acoustic model, a likelihood that the segment of the speechinput that was determined to correspond to the word of the word pairthat appeared in the top result instead corresponds to the other word ofthe word pair (including, in some cases, determining whether the segmentcorresponds to a null word by determining, for example, whether thespeech input corresponds to a combination of other words without adiscrete word for the segment of speech input from which the word of thepair that appeared in the top result was determined). When thelikelihood determined from the acoustic model is above a threshold, thismay be indicative of a potential significant error in the toprecognition result and the evaluation engine may trigger an alert.

Similarly, as another example, the evaluation engine may determine, whenone word of a word pair in an alert list appears in a top result,whether a language model indicates a likelihood above a threshold thatthe other word of the word pair may have been used in the speech inputrather that the word that appeared in the top result and thus alikelihood that a correct recognition of the speech input includes theother word of the pair. This may be done in any suitable way. Forexample, the evaluation engine may form a new alternative recognitionresult by replacing the word of the word pair that appeared in the toprecognition result with the other word of the pair (including, in somecases, replacing with a null word) and then analyzing the newalternative recognition result using a language model to determine alikelihood of the new alternative recognition result appearing in thelanguage and/or domain to which the language model corresponds. When thelikelihood determined from the language model is above a threshold, thismay be indicative of a potential significant error in the toprecognition result and the evaluation engine may trigger an alert.

The language model that is used in this way by an evaluation engine maybe any suitable language model. The language model may be the samelanguage model as was used by the ASR system in producing therecognition results or a different language model. In some embodiments,the language model may be a standard language model for a language(e.g., the English language) and in other embodiments the language modelmay be a language model specific to a domain and that takes into accountthe consequences of potential significant errors in that domain. Thelanguage model may account for the consequences of potential significanterrors by weighting likelihoods of words appearing, such as by adjustingthe likelihoods to be higher when the words are associated withsignificant consequences in that domain (e.g., significant consequencesfor a patient in a medical domain). The likelihood for a word may beweighted based on information indicating the significance of theconsequences in the domain when the word is incorrect in recognitionresults for the domain, including when the word is misrecognized foranother word with which the word is paired. Weighting the likelihoods inthis way may result in likelihoods that are above the threshold by whichthe likelihoods are evaluated even for expressions that occurinfrequently, and thereby result in the triggering of an alert regardingthe words. For example, where a likelihood of a string of wordsincluding the word “malignant” is low, but the medical consequences ofmisrecognizing this string of words are serious, the language model mayproduce a weighted likelihood that indicates a high likelihood of thisstring of words appearing in the domain language.

In some embodiments that use a language model for a language and/or fora domain that does not weight likelihoods based on significance ofconsequences of misrecognition of a word, an evaluation engine mayperform such a weighting of likelihoods. For example, the evaluationengine may receive a likelihood of a recognition result appearing in alanguage and/or domain from a language model and may weight thatlikelihood, in the manner described above, according to informationindicating a significance of consequences of misrecognition. In somesuch embodiments, once the evaluation engine has weighted thelikelihood, the evaluation engine may evaluate the weighted likelihoodto determine whether to trigger an alert.

Embodiments that evaluate likelihoods using an acoustic model or alanguage model and trigger an alert based on the likelihoods from themodel may do so to increase the chances of indications of potentialsignificant errors being identified, alerts being triggered, and errorsbeing corrected during a review process. In some cases, this mayincrease the number of “false” alerts that are not errors that arepresented to a reviewer. However, by including alerts regarding morepotential errors that change a meaning of a recognition result in a waythat would cause significant consequences in the domain when incorrect,the chance of a significant error being missed and the consequences of asignificant error occurring may be lower.

In some embodiments, evaluation techniques that evaluate likelihoodsusing an acoustic model or language model may be used to identifypotential significant errors in recognition results that are aconsequence of misrecognition by an ASR system. In addition, or in thealternative in some embodiments, evaluation techniques that evaluatelikelihoods using an acoustic model or language model may be used toidentify potential significant errors in recognition results that resultfrom the speaker. The speaker may have misspoken by speaking the wrongword/phrase, speaking an extraneous word/phrase, neglecting to speak aword/phrase, or in any other way. Determining whether a recognitionresult includes an indication of a potential significant error resultingfrom erroneous speech input may be carried out in substantially the samemanner as the above-described process of using an acoustic and/orlanguage model to determine whether a recognition result includes anindication of a potential significant error resulting frommisrecognition by an ASR system. The above-described process compares atop recognition result to a set of one or more pairs of words/phrasesand determines, when one word or phrase of a word/phrase pair appears inthe top result, whether a language model indicates a likelihood above athreshold that the other word/phrase of the word/phrase pair may havebeen used in the speech input rather that the word that appeared in thetop result. An evaluation engine may additionally or alternativelydetermine, using similar acts, when one word/phrase of a word/phrasepair appears in the top result, whether a language model indicates alikelihood above a threshold that the other word/phrase of theword/phrase pair could have appeared in the speech input and thuswhether a speaker might have intended to speak the other word/phrase ofthe pair. When the likelihood, determined from the language model, thatthe other word/phrase could have appeared in the speech input is abovethe threshold, this may be indicative of a potential significant errorin the top recognition result and the evaluation engine may trigger analert.

FIG. 5 below illustrates an example of techniques that an evaluationengine may use to evaluate recognition results using word pairs and alanguage model to determine, when one word of a pair appears in a toprecognition result, a likelihood that the speech input instead includedthe other word of the pair. As will be appreciated from the discussionbelow, the process of FIG. 5 is described as being executed togetherwith the process 400 of FIG. 4. However, it should be appreciated thatthe process of FIG. 5 is not limited to being carried out together withthe process of FIG. 4 and that embodiments may implement the processesof FIGS. 4-5 separately.

Thus, described below in connection with FIGS. 4-5 are varioustechniques that an evaluation engine may use to evaluate recognitionresults using word pairs. In embodiments that evaluate recognitionresults using a list of word pairs, the words that are included in thelist of word pairs may be any suitable words for which it is desired togenerate an alert. For example, in some embodiments, the word pairs inthe list may be limited to pairs of words where a misrecognition mighthave serious consequences (e.g., in a particular domain) if notcorrected.

FIG. 4 illustrates one illustrative process that may be used in someembodiments by an evaluation engine to evaluate recognition results todetermine whether the recognition results include an indication of apotential error that is semantically significant. The process 400 ofFIG. 4 begins in block 402, in which an evaluation engine receives alist of the N best recognition results produced by an ASR system duringa recognition process performed on a speech input. As mentioned above,the N best list may include a top result and N−1 alternative recognitionresults.

Following block 402, the evaluation engine carries out a loop evaluatingeach word of the words included in the top recognition result of the Nbest recognition results. During the loop, each word of the top resultmay be evaluated iteratively, one at a time, or in any other suitableway. During the loop, the evaluation engine in block 404 selects thenext word of the top result that has not yet been evaluated anddetermines in block 406 whether the selected word matches either word ofany of the word pairs to which the top recognition result is to becompared. The determination of block 406 may be made in any suitablemanner, including by looping through a set of word pairs and comparingthe selected word to each word of the word pairs, by taking a hash ofthe selected word and searching hashes of the word pairs based on thehash of the selected word to determine whether the selected word appearsin the word pairs, or in any other manner. For any word pairs for whichthe selected word matches one word of the pair, in block 408 theevaluation engine determines whether the other word of the pair appearsin any of the alternative recognition results.

The determination of block 408 may be made by the evaluation engine inany suitable manner. In some embodiments, the evaluation engine maydetermine whether any of the alternative recognition results include theother word of the pair at any position within the result. In some cases,though, merely considering whether the other word of the pair appears atany position within the result may lead to false alerts that do notindicate a potential error in the top result (e.g., where the two wordsof the pair appear at different word positions between the top resultand the alternative recognition result). The word pair, which may be apair of acoustically-confusable words, is being used in block 408 todetermine whether the ASR system correctly determined, for the topresult, that a section of speech input corresponds to a word of thepair. If the ASR system determines, for the top result, that a firstsegment of the speech input corresponds to the first word of a wordpair, and determines for an alternative result that a different segmentof the speech input corresponds to the second word of the pair, this maynot indicate anything about a potentially significant error. Therefore,in some embodiments, when one word of a word pair appears in a topresult, the evaluation engine may determine in block 408 whether any ofthe alternative recognition results include the other word of the pairas a word corresponding to the same segment of the speech input as theword of the pair that appeared in the top result. This may be done inany suitable manner, as embodiments are not limited in this respect.

Some ASR systems may produce, for each word or phrase of a recognitionresult, an indication of which sounds in the speech input the ASR systemidentified as corresponding to the word/phrase. The indication that isproduced may be in the form of a time or time range in the speech input,which indicates that the sounds in the speech input at that time or timerange were identified by the ASR system as corresponding to theword/phrase. In block 408, the evaluation engine may determine whetherthe time or time range of the speech input that the ASR systemidentified, for the top result, as corresponding to the word that wasselected in block 404 was also identified by the ASR system, for analternative result, as corresponding to the other word of the word pair.Determining that the times or time ranges for the words are the same mayaid the evaluation engine in determining that the words of the word pairappearing in the recognition results were determined by the ASR systemto correspond to the same segment of speech input and thus areindicative of a potential significant error in the top recognitionresult. In some embodiments, the evaluation engine may not determinewhether there is an exact correspondence between the times or timeranges for the words of the word pair, but may determine in block 408whether the times or time ranges are within a margin (e.g., plus orminus one second or any other suitable margin).

Some ASR systems do not produce a time indication for words ofrecognition results. In some embodiments that operate with ASR systemthat do not produce time indications, dynamic phonetic alignmenttechniques may be used to align the top result and the alternativerecognition results and thereby align words that correspond to sounds inthe speech input. In embodiments that use dynamic phonetic alignment,once the recognition results have been aligned, the evaluation enginemay determine whether the word selected in block 404 is aligned with theother word of a word pair. By determining whether the words of the wordpair appear in the top result and an alternative result and that thewords are aligned, the evaluation engine can determine whether the wordswere determined to correspond to the same segment of speech input andthus are indicative of a potential significant error in the toprecognition result.

Regardless of how the determination is made in block 408, if theevaluation engine determines that the second word of the pair appears inan alternative recognition result (and optionally in an alignedposition), the presence of the two words (which may be acousticallysimilar and confusable words) may be indicative of a potential error inthe recognition results that may be semantically significant.Accordingly, in block 410 the evaluation engine triggers an alertregarding the top result and, in block 412, the evaluation engineoptionally stores information about the potential significant error. Anysuitable information may be stored in block 412, including informationidentifying the alternative recognition result that included the otherword of the pair that indicated the presence of the potentialsignificant error in the top result. The triggering and storing inblocks 410, 412 may be carried out in any suitable manner, includingaccording to any of the techniques described above in connection withblocks 208, 210 of FIG. 2 or in other ways.

After the triggering and storing of blocks 410, 412, or when it isdetermined in blocks 406, 408 that both words of a pair were notincluded in the recognition results, the evaluation engine determines inblock 414 whether there are more words of the top recognition result tobe evaluated. If so, the evaluation engine returns to block 404 toselect another word of the top result and begin a new iteration of theloop by evaluating the recognition results using the selected word. Whenit is determined that there are no more words of the top result to beevaluated, the process 400 ends.

As discussed above, embodiments are not limited to evaluating therecognition results using word pairs to determine whether both words ofa word pair appear in the N best recognition results. Rather, in someembodiments, an evaluation engine may evaluate the top recognitionresult using a word pair and an acoustic and/or language model todetermine, when one word of a word pair appears in the top recognitionresult, a likelihood that the speech input (from which the toprecognition result was produced) instead included the other word of thepair. FIG. 5 shows an illustrative process in accordance with theseembodiments.

The process 500 of FIG. 5 may, in some embodiments, be carried outtogether with the process 400 of FIG. 4, such as by being carried outwithin the loop of the process 400. Accordingly, prior to the start ofthe process 500 of FIG. 5, an evaluation engine may receive the list ofN best recognition results from an ASR system as in block 402 of FIG. 4and may select, as in block 404 of FIG. 4, a word pair by which toevaluate the top recognition result for indications of potentialsignificant errors. Additionally, in some embodiments, prior to thestart of the process 500 of FIG. 5, an evaluation engine may determine,as in blocks 406, 408 of FIG. 4, that the list of N best recognitionresults do not include both words of the currently-selected word pair.However, it should be appreciated that embodiments are not limited toimplementing the process 500 of FIG. 5 together with the process 400 ofFIG. 4 or, where the two processes are implemented together, tointegrating the process 500 with the process 400 in any particularmanner.

The process 500 begins in block 502, in which an evaluation engineidentifies, for a currently-selected word pair, a position in the toprecognition result of one of the words of the word pair. In block 504,the evaluation engine replaces the word of the word pair that appears atthat position with the other word of the word pair. Replacing the wordthat appears in the top recognition result with the other word of theword pair creates a new string of words that is possibly a newrecognition result. In block 506, the evaluation engine uses a languagemodel, which corresponds to a language and/or domain of the speechinput, to determine a likelihood of the string of words of the newrecognition result appearing in speech/text in the language or domain.Upon receiving the likelihood, in block 508 the evaluation enginecompares the likelihood to a threshold to determine whether thelikelihood of the new recognition result appearing in the language ordomain is above the threshold.

When the likelihood of the new recognition result is above thethreshold, this indicates that the word of the pair that did not appearin the top recognition result, but was inserted to form the newrecognition result, is sufficiently likely to have appeared in thespeech input instead of the word that appeared in the top result for anactions to be taken. In other words, when the likelihood is above thethreshold, it is sufficiently likely that the word of the pair appearedin the top recognition result in error. Further, because the words ofthe word pair are those words that would have serious consequences ifone was misrecognized as the other, the likelihood being above thethreshold is indicative of a potential significant error in the toprecognition result. Accordingly, if the evaluation engine determines inblock 508 that the new recognition result produced in block 506 issufficiently likely to appear, then in block 510 the evaluation enginetriggers an alert regarding the potential significant error andoptionally stores information identifying the potential significanterror, which may include information identifying the other word of theword pair and the position at which the other word was determined to besufficiently likely to appear. The triggering and storing in block 510may be carried out in any suitable manner, including according to any ofthe techniques described above in connection with blocks 208, 210 ofFIG. 2 or in other ways.

Once the alert has been triggered and information stored in block 510,or if the new recognition result of block 506 was determined in block508 not to be sufficiently likely to appear in the language or domain,the process 500 ends.

In the process 500 described above, the evaluation engine determineswhether a likelihood of the new recognition result appearing in thedomain is above a threshold to determine whether to trigger an alert. Itshould be appreciated that embodiments that implement a process forevaluating likelihoods of recognition results generated by replacingwords of the top recognition result are not limited to evaluatinglikelihoods using a threshold to determine whether to trigger an alert.In some embodiments, rather than evaluating the likelihood using athreshold, an evaluation engine may compare a likelihood of the newrecognition result appearing to a likelihood of the top recognitionresult appearing. Based on the comparison of the likelihoods, theevaluation engine may determine whether to trigger an alert. Forexample, if the likelihood of the new recognition result is much lowerthan the likelihood of the top recognition result, the evaluation enginemay determine that an alert should not be triggered. Determining whetherthe likelihood of the new recognition result is much lower than thelikelihood of the top recognition result may include, for example,determining whether the likelihood of the new recognition result is morethan 10% less likely than the top recognition result, less than half aslikely as the top recognition result, less than one-tenth as likely asthe top recognition result, or has any other suitable relative value ascompared to the likelihood of the top recognition result. Embodimentsthat implement a process for evaluating likelihoods of recognitionresults generated by replacing words of the top recognition result arenot limited to comparing likelihoods of recognition results in anyparticular manner.

In embodiments that evaluate recognition results using word pairs and alanguage model as in the examples of FIG. 5, any suitable language modelor models may be used. In some embodiments, the language model may be astandard language model for a language (e.g., the English language) orfor a domain (e.g., the medical domain). It may be the same languagemodel(s) used by the ASR system in producing the N best recognitionresults based on the speech input. In some embodiments, as mentionedabove, the language model(s) may weigh likelihoods of one or more wordsappearing in the language or domain at least in part based oninformation identifying the significance of the consequences ofmisrecognizing the word(s), including misrecognizing one paired word foranother. The information may indicate the consequences of misrecognitionin general or the consequences in a particular domain. For example, alanguage model may be related to a medical domain and, in addition toindicating a likelihood of words appearing in the medical domain, mayweigh the likelihood of the words appearing based on informationindicating the significance of the consequences in the medical domain ifthose words are misrecognized. Weighting the likelihood may includeincreasing the likelihood of the words appearing in the language ordomain. By increasing the likelihood based on the consequences ofmisrecognition (e.g., when the words would cause serious medicalconsequences if misrecognized), the likelihood of the words appearingmay be increased and may be more likely to exceed the thresholds againstwhich the likelihoods are compared in the process of FIG. 5. When thelikelihood exceeds the threshold, an alert is triggered and a reviewermay review the words more carefully. Increasing the likelihood when thewords would cause serious consequences if misrecognized thereforeincreases the chances that the threshold would be exceeded, an alertwould be triggered, and a reviewer would review the words morecarefully, which may reduce the chances of the serious consequencesoccurring from a misrecognition.

Additionally, as discussed above, in some embodiments an evaluationengine may receive a likelihood of a recognition result appearing in adomain from a language model that does not perform such a weighting, andthe evaluation engine may weigh the likelihood in the manner describedabove. Once the evaluation engine has weighed the likelihood based oninformation indicating a significance of consequences of misrecognitionin the domain, the evaluation engine may evaluate the likelihood todetermine whether to trigger an alert.

As discussed above, it should be appreciated that, in some embodiments,a word pair that is evaluated by the processes 400, 500 of FIGS. 4 and 5may include a null word (i.e., silence or no word). In some embodiments,a word pair that includes a null word may be evaluated just as any otherword pair. In other embodiments, though, a word pair that includes anull word may be considered a special case that is processed usingoperations that may be different in some ways from the operations usedto evaluate other word pairs. The process 600 of FIG. 6 illustratesoperations carried out in some embodiments when a word pair includes anull word, which operations may be similar in some ways to operations ofprocesses 400, 500 of FIGS. 4-5 discussed above.

The process 600 of FIG. 6 may be carried out together within a loopevaluating word pairs and recognition results, such as the loopdescribed above in connection with FIG. 4. Accordingly, prior to thestart of the process 600 of FIG. 6, an evaluation engine may receive alist of N best recognition results from an ASR system (such as in block402 of FIG. 4) and may select (such as in block 404 of FIG. 4) a wordpair by which to evaluate the N best recognition results for indicationsof potential significant errors. The word pair that is selected includesa null word and a non-null word.

The process 600 begins in block 602, in which the evaluation enginedetermines whether a non-null word paired with a null word in thecurrently-selected word pair appears in any of the N best searchresults, including either the top recognition result or any of the N−1alternative recognition results. The determination of block 602 can beconsidered to serve the same function as the two determinations ofblocks 406, 408 of FIG. 4. The blocks 406, 408 determined whether thewords of a word pair appear in both the top recognition result and oneof the alternative recognition results. In block 602, however, theevaluation engine need only determine whether the non-null word appearsin any of the N best recognition results and may not evaluate words ofthe recognition results to determine if a null word appears, as everyresult necessary includes a null word. This is because a null word“appears” in all recognition results (i.e., at least at the beginningand end of a recognition result, and between each of the words of arecognition result when the recognition result includes multiple words).In embodiments that evaluate a word pair that includes a null word todetermine whether both of the words of the pair appear in therecognition results, the null word will always be determined to appear.Thus, to determine whether both words of a word pair appear in the Nbest recognition results, it may be sufficient to determine whether thenon-null word appears in any of the N best recognition results.

If the evaluation engine determines in block 602 that the non-null wordappears in any of the N best recognition results, then in block 608 theevaluation engine triggers an alert and optionally stores informationidentifying the potential significant error, which may includeinformation identifying the word and the position of the recognitionresult at which it appeared. The triggering and storing of block 608 maybe carried out in any suitable manner, including as described above inconnection with block 208, 210 of FIG. 2 or in other ways.

If, however, in block 602 the evaluation engine determines that thenon-null word does not appear in any of the N best results, then (as inthe process 500 of FIG. 5) the evaluation engine uses a language modelto determine whether the non-null word was sufficiently likely to haveappeared in the speech input from which the top recognition result wasproduced, even though the non-null word does not appear in N bestrecognition results.

Since the null word that is paired with the non-null word that appearsat multiple positions in the top recognition result (i.e., at thebeginning and end of the result, and between words of the result),replacing the null word with the non-null word (as in block 504 of FIG.5) includes iteratively replacing each occurrence of the null word inthe top recognition result with the non-null word and determining thelikelihood of the resulting new recognition result using the languagemodel. In other words, in block 604, the evaluation engine iterativelyinserts the non-null word of the pair into the top recognition resultinto each word position of the top recognition result (i.e., into thebeginning of the top recognition result, then between the first word andsecond word, then the second and third words, etc.) and for eachinsertion uses the language model to determine a likelihood that theresulting string of words would appear in the language or domain towhich the language model relates. The evaluation engine then compares,in block 606, each of these likelihoods to a threshold to determinewhether, for each string of words that includes the non-null word, thelikelihood of the string of words appearing in the language or domain isabove the threshold.

When the likelihood of one of the strings of words is above thethreshold, this indicates that the non-null word is sufficiently likelyto have appeared in the speech input at the position into which thenon-null word was inserted into the top recognition result. When thenon-null word is sufficiently likely to have appeared in the position,this may be indicative of a potential error in the top recognitionresult. Further, because the words of the word pair may be those wordsthat would have serious consequences if misrecognized, when the non-nullword is sufficiently likely to have appeared in the speech input at theposition, this may be indicative of a potential significant error in thetop recognition result. Accordingly, if the evaluation engine determinesin block 606 that that the non-null word is sufficiently likely to haveappeared in any of the positions, then in block 608 the evaluationengine triggers an alert regarding a potential significant error andoptionally stores information identifying the potential significanterror, which may include information identifying the non-null word andthe position at which the non-null word was determined to besufficiently likely to appear. The triggering and storing in block 608may be carried out in any suitable manner, including according to any ofthe techniques described above in connection with blocks 208, 210 ofFIG. 2 or in other ways.

Once the alert has been triggered and information optionally stored inblock 608, either in response to identifying in block 602 that thenon-null word in any of the N best recognition results or determining inblock 606 that the non-null word was sufficiently likely to haveappeared in a position of the top recognition result, or if it wasdetermined in block 606 that the word was not sufficiently likely toappear, the process 600 ends.

The processes 400-600 of FIGS. 4-6 were described as processingrecognition results in an N best list format. As discussed above,embodiments are not limited to processing recognition results in an Nbest list format. In embodiments that operate with recognition resultsin other formats, such as a lattice format, other processes may becarried out on the recognition results to implement techniques describedin connection with FIGS. 4-6. For example, an evaluation of the words ofthe recognition results to word pairs may be carried out usingrecognition results in a lattice format. In some embodiments, a latticeformat may be advantageous for evaluating the recognition results usingword pairs, as the lattice will identify directly in the format of thelattice the words that were identified by the ASR system as alternativesthat may each correspond to the same sounds of the speech input. Asmentioned above, the lattice format includes directed interconnectionsbetween nodes of the lattice and the directed interconnections identifypaths through the lattice that each correspond to a recognition result.The paths through the lattice may overlap when multiple recognitionresults include the same word(s) corresponding to the same sounds fromthe speech input. When the paths through the lattice diverge todifferent nodes, this indicates that the ASR system has identifieddifferent words as corresponding to the same sounds of the speech input.The different words are aligned alternatives for the same sounds of thespeech input. The lattice can therefore be evaluated to determinewhether, for the parts of the lattice that include alternative words,whether the word for the top recognition result appears in a word pairand, if so, whether one of the alternative words is the other word ofthe word pair. The lattice may also be evaluated using word pairs and alanguage model to determine whether, if any of the words of the wordpairs appears in the lattice, the other word of the pair can besubstituted in the paths of the lattice through the node for that wordto create a string of words that is sufficiently likely to appear in thelanguage/domain. It should be appreciated, however, that embodimentsthat operate with a lattice are not limited to operating with a latticein the manner described above, and that embodiments may process alattice and word pairs in any suitable manner.

In the examples of FIGS. 4-6, an evaluation engine uses pairs of wordsto determine whether there is an indication of a potential error in therecognition results. The pairs of words that are evaluated in theseprocesses, or in any other process operating in accordance withtechniques described herein, may be determined in any suitable manner,as embodiments are not limited in this respect.

In some embodiments, the pairs of words may be determined through amanual process, in which one or more people identify pairs of words(including null words) that may be confused by an ASR system and mighthave significant consequences if misrecognized. After the pairs of wordsare manually assembled, the evaluation engine may use the pairs in anyof the above-described processes or any other process.

In some embodiments, as an alternative to or in addition to a manualprocess, an automated process of determining word pairs may be carriedout. Automated processes that may be used in some embodiments maydetermine words that might be confused by an ASR system (e.g., becauseof acoustic similarity), the confusion of which might have significantconsequences, and may pair these words (including nulls) together in aset of word pairs. Automated processes may determine whether confusionmight have significant consequences in any suitable way. For example,the processes may determine confusable words that have differentmeanings or that change a meaning of a phrase or sentence when includedin the phrase/sentence. For example, some automated processes mayidentify words, phrases, or sentences that have meanings that areinconsistent (e.g., meanings that are opposites of one another) or ofdifferent degrees of specificity (e.g., pneumonia and acute pneumonia).For example, an automated process might determine that the phrases “thegrowth is malignant” and “the growth is nonmalignant” have related,opposite meanings and determine that the opposite meanings is based onthe words “malignant” and “nonmalignant.” An automated process mightalso determine that the phrases “there is evidence of active disease”and “there is no evidence of active disease” have related, oppositemeanings and that the opposite meanings is based on the word “no.” Someautomated processes, when evaluating these phrases, may then pair thewords “malignant” and “nonmalignant” and a null word and “no” based onthis evaluation.

FIG. 7 illustrates one process that may be used in some embodiments by apairing engine to evaluate phrases and determine pairs of words that arerelated in meaning. The process 700 of FIG. 7 begins in block 702, inwhich the pairing engine receives a corpus of text in a language, wherethe corpus includes multiple words and/or phrases. The corpus of textmay be any suitable corpus including any suitable text, such as a set oftexts of medical journal publications, medical reports, novels,newspaper and magazine articles, blog posts, and/or any other texts.

In block 704, the pairing engine semantically interprets each phrase ofthe corpus of text to identify one of more significant facts expressedin the phrases. The significant facts may be any suitable set ofsignificant facts, including facts significant for a particular domain.The semantic interpretation of block 704 may be carried out in anysuitable manner. In some embodiments, for example, the pairing enginemay interact with a semantic interpretation engine that is configured tointerpret text in a language matching that of the corpus to determinesignificant facts expressed in each of the phrases. A semanticinterpretation engine may be implemented in any suitable manner, oneexample of which was discussed above in connection with the semanticinterpretation engine 106D of FIG. 1.

In block 706, the pairing engine identifies pairs of phrases for whichthe significant facts expressed in the phrases have opposite meanings.The determination in block 706 may be made in any suitable manner. Insome embodiments, for example, an index of significant facts that can beidentified by the semantic interpretation facility and relationshipsbetween those facts may be predefined. The predefined relationships mayidentify significant facts that have opposite meanings. Where suchpredefined relationships are used, in block 706 the significant factsthat were identified in block 704 from the semantic interpretation ofthe phrases of the corpus may be evaluated using the predefinedrelationships to identify any significant facts that have oppositemeanings. When significant facts that have opposite meanings areidentified, the phrases from which the significant facts were identifiedby the semantic interpretation engine may be identified as havingopposite meanings.

The phrases that are identified as having opposite meanings may then beused in block 708 to determine words that have opposite meanings or thatcause phrases to have opposite meanings. These words may be identifiedby determining, from the phrases that have opposite meanings the wordsof the phrases that result in the phrases having opposite meanings. Thewords can be identified in block 708 in any suitable manner, asembodiments are not limited in this respect.

In some embodiments, the pairing engine may identify the words in block708 by modifying the phrases identified as having opposite meanings inblock 706. The modification may be carried out in any suitable manner,including by iteratively removing words or combinations of words fromeach of the phrases and semantically interpreting the modified phrases.Once a modified phrase (corresponding to one of a pair of phrases havingan opposite meaning) is semantically interpreted, the pairing engine maydetermine whether the modified phrase expresses the significant factthat was identified as having a meaning opposite to that of asignificant fact of another phrase. If the modified phrase is determinedto express the significant fact, the pairing engine can determine thatthe word(s) that cause the phrase to express the significant fact areincluded in the modified phrase. Through iteratively modifying thephrase and identifying what modifications lead a modified phrase toexpress the significant fact and which lead the modified phrase to notexpress the significant fact, the pairing engine can determine theword(s) that correspond to the significant fact. When the pairing enginecarries out this process for each of a pair of phrases that haveopposite meanings, the pairing engine can determine a pair of series ofone or more words, such as a pair of words and/or phrases, that leadphrases to have opposite meanings. Such a pair of series of words may beindicative of a potential error in recognition results when both seriesof words of the pair are present in recognition results derived from asame speech input. For example, when a top recognition result includesone series of words of a pair and an alternative recognition resultincludes the other series of words of a pair, this may be indicative ofa potential error in the recognition results.

It should be appreciated that while two series of words determined inthis manner are not identical, there may be some overlap between the twoseries of words. When the two series of words determined fromrecognition results include multiple words, in some cases one or more ofthe words of a first series of words may appear in the second series ofwords. In other cases, however, none of the words of a first series mayappear in a second series.

In some embodiments, the modification of phrases described above may notbe performed for all phrases that have opposite meanings, but insteadmay only be done for phrases that have similar wording and/or similarsounds. This is because performing a modification of phrases withoutsimilar wording/sound to determine the words of those phrases thatresult in different meanings could lead to pairs of words that aredissimilar and not acoustically confusable and thus are unlikely to beconfused by an ASR system. For example, while the phrases “there is noevidence of active disease in the abdomen” and “disease present in theabdomen” may be identified in block 706 as phrases having oppositemeanings, the words of the phrases are dissimilar. Any attempt atiteratively removing words to determine the words that cause themeanings to be opposite would thus identify words that are unlikely tobe confused by an ASR system, such as the acoustically-dissimilar words“no” and “present.” Words that are unlikely to be confused by an ASRsystem, in some embodiments, may not be very useful to be associated asa pair and used to evaluate recognition results. This is because whenthe words are unlikely to be confused by the ASR system, the words arealso unlikely to be associated with a misrecognition by the ASR systemdue to such confusion and unlikely to be a source of a potentialsignificant error.

Accordingly, in some embodiments, a pairing engine may select from thephrases identified in block 706 as having opposite meanings pairs ofphrases that are similar in wording or similar in the sounds used toexpress the phrases. The pairing engine may use any suitable techniquefor comparing wording of phrases or sounds for phrases, as embodimentsare not limited in this respect. For example, a corpus of text mayinclude the phrases “there is no evidence of active disease in theabdomen,” “no disease in the abdomen,” “disease present in the abdomen,”and “there is evidence of active disease in the abdomen,” and a pairingengine may identify that the first two phrases have a meaning that isthe opposite of the latter two phrases. The pairing engine may thendetermine that the pair of phrases “there is no evidence of activedisease in the abdomen” and “there is active disease in the abdomen,”and the pair of phrases “no disease in the abdomen” and “disease presentin the abdomen,” have similar wording and/or sounds.

Determining whether the pairs of phrases have similar wording and/orsounds may be done in any suitable manner, including by comparingwording and/or by comparing sounds. For example, pairs of phrases may beconsidered similar when the majority of the words included in one phraseappear in the other phrase in the same order and position as in thefirst phrase. As another example, pairs of phrases may be consideredsimilar when the phrases are acoustically confusable. A pair of phrasesmay be acoustically confusable when the majority of sounds in one phraseappear in the other phrase in the same order and position as in thefirst phrase. Any suitable technique may be used to determine whetherpairs of phrases are similar in wording and/or sound.

Once pairs of phrases similar in wording and/or sound are identified,the iterative process described above of removing words/phrases fromeach phrase of the pair of phrases and semantically interpreting theresulting phrase can be carried out for the identified pairs. Throughthe iterative process, pairs of words that cause the pairs of phrases toexpress different significant facts can be identified and, because thewords were identified from phrases that are similar in wording and/orsound, the identified pair of words includes words that an ASR systemmay confuse. In some embodiments, once the pairs of words are identifiedby the pairing engine from the iterative process, the pairing engine mayfurther determine whether the words of the pair may be confused by anASR system by determining whether the words are acoustically similar andacoustically confusable. If the words of a pair are not acousticallysimilar, then the pair of words may be rejected as unlikely to beconfused by an ASR system.

In block 710, after the pairing engine has determined in block 708 oneor more pairs of words that cause phrases to have opposite meanings andthat may be confused by an ASR system, the pairing engine may add thepairs to a set of pairs of words that may be used by an evaluationengine in evaluating recognition results. Once the pairs of words havebeen added to the set, the process 700 ends.

It should be appreciated that embodiments that include an automatedprocess for identifying pairs of words that, when present in recognitionresults, are indicative of a potential significant error in therecognition results are not limited to carrying out the automatedprocess of FIG. 7 and that other processes are possible. For example,while the exemplary process 700 of FIG. 7 could be used to identifywords that are associated with opposite meanings of phrases, otherembodiments may implement a process that identifies words that areassociated with any other suitable relationship between meanings ofphrases, including words that are associated with any suitable type ofinconsistent meaning other than opposite meanings (e.g., differentdegrees of specificity). Embodiments are also not limited to identifyingwords associated with one type of relationship between meanings ofphrases, as some embodiments may identify words associated with multipletypes of relationships between meanings of phrases.

Additionally, as discussed above, it should be appreciated that while,for ease of description, the examples of FIGS. 4-7 were discussed interms of word pairs, embodiments that evaluate words of recognitionresults to determine whether there are indications of potential errorsthat may be semantically meaningful are not limited to operating withpairs of words. Some embodiments may evaluate recognition results usingpairs of words and/or phrases, which may include word/word pairs,phrase/phrase pairs, and/or word/phrase pairs.

It should also be appreciated that while the examples above werediscussed in terms of pairs of words and/or phrases, embodiments thatevaluate recognition results using sets of words and/or phrases todetermine whether there are indications of potential significant errorsare not limited to operating with pairs of words and/or phrases.Instead, embodiments may operate with sets of words/phrases of anysuitable size. For example, embodiments may evaluate the recognitionresults using sets of three or more words and/or phrases. When sets ofwords/phrases larger than two (i.e., larger than pairs) are used,processes like the ones discussed above may be used that determine, whenone word/phrase of a set appears in the top result, whether any of theother words/phrases of the set appear in the alternative recognitionresults.

Various processes have been discussed for detecting indications ofpotentially significant errors in recognition results by comparingrecognition results or contents of and/or information about recognitionresults. For example, in connection with FIG. 3, a comparison of factsextracted from recognition results was used to determine whether therewere indications of potentially significant errors in recognitionresults, and in connection with FIGS. 4-6, a comparison of wordsincluded in recognition results was used to determine whether there wereindications of potentially significant errors in recognition results. Itshould be appreciated, however, that embodiments are not limited toreviewing and comparing top recognition results and alternativerecognition results to determine whether there are indications ofpotential significant errors in the recognition results. In someembodiments, for example, an evaluation engine may determine whether oneor more words included in the top recognition result are themselvesindicative of a potential error in the top result.

In some embodiments, the top recognition result may be evaluated forwords that are unlikely to appear in the speech input for a particulardomain. For example, in a medical context, the word “pizza” may beunlikely to appear in a speech input. The appearance of the word “pizza”in a recognition result in a medical context (e.g., “there is evidenceof active pizza in the abdomen”) may therefore be an indication of apotential error in the recognition result.

FIG. 8 illustrates an exemplary process 800 that may be used in someembodiments to review a top recognition result generated by an ASRsystem to determine whether the top recognition result includes one ormore words that are identified as being unlikely to appear in the domainto which the speech input relates. The words unlikely to appear may beidentified in any suitable way. In some embodiments, prior to the startof the process 800, a set of words that are unlikely to appear may havebeen identified in any suitable manner, such as through a manual and/orautomated process. In some embodiments, a word may be determined to beunlikely to appear in a domain through a manual and/or automatedanalysis of a likelihood of the word appearing in the domain. Forexample, in some such embodiments, when it is determined that thelikelihood of the word or phrase appearing is below a threshold, theword or phrase may be determined to be sufficiently unlikely to appearin the domain for the word or phrase to be flagged as unlikely.

The process 800 begins in block 802, in which an evaluation enginereceives the top recognition result produced by an ASR system for aspeech input. In block 804, the evaluation engine reviews words of thetop result to determine whether the top result includes any of a set ofone or more words identified as being unlikely to appear in the domainto which the top recognition result relates. In block 806, theevaluation engine determines whether any of the words were found and, ifso, proceeds to block 808, in which an alert regarding the top result istriggered. In addition, information identifying the one or more wordsthat were identified as being unlikely to have appeared in the domain isstored, although storing this information is optional and not performedin all embodiments. The triggering and optional storing in block 808 maybe carried out in any suitable manner, including according to any of thetechniques discussed above in connection with blocks 208, 210 of FIG. 2.

Once the evaluation engine has triggered an alert in block 808, or if itis determined in block 806 that no words of the top result wereidentified as being unlikely to appear in the domain, the process 800ends.

The process 800 may be used to identify any words that are not likely tobe included in a speech input for any domain to which the speech inputrelates. In one example given above, a domain may be the “medical”domain. It should be appreciated, however, that embodiments are notlimited to any particular domains. Further, it should be appreciatedthat embodiments are not limited to any particular type of domain orbreadth of subjects for a domain. For example, while “medicine” may be adomain in some embodiments, in other embodiments a more focused domainmay be specified, such as “abdominal examination” or “CT procedure” or“x-ray of left leg.” For a more focused domain, a set of words that areidentified as unlikely to appear in the domain might include words thatare likely to appear in other domains to which the focused domainrelates. For example, while the word “leg” might be considered common orlikely to appear for a “medicine” domain, the word “leg” might beconsidered uncommon or unlikely to appear in an “abdominal examination”domain. As another example, while the phrase “right leg” might beconsidered common or likely to appear for a “medicine” domain, thephrase “right leg” might be considered unlikely to appear in an “x-rayof left leg” domain. As a third example, while the number seventeen maybe common in the “medicine” domain, it may be uncommon in the “bloodpressure reading” domain.

It should also be appreciated that embodiments are not limited toevaluating a recognition result based on only one domain and words thatare unlikely to appear in that domain. In some embodiments two or moredomains may be evaluated to determine if the speech input includes wordsunlikely to appear in either domain. For example, in some embodiments aspeech input may relate to both the domains “pelvic exam” and “medicalexamination of a female,” when the speech input regards a pelvic exam ofa woman. In the domain “pelvic exam,” the word “prostate” may not beuncommon or considered to be unlikely to appear in a speech input.However, the word “prostate” might be considered uncommon in the“medical examination of a female” domain. Accordingly, in embodiments inwhich a set of unlikely words is created from a union of the sets ofunlikely words for two domains, a word for either domain is included inthe set and identified as an indication of a potential error whenincluded in a recognition results for a speech input that relates tothese two domains.

Embodiments are not limited to operating with any particular domains norto operating with a fixed domain or fixed set of unlikely words for eachrecognition result. Rather, in some embodiments, an evaluation enginemay select, for each recognition result to be evaluated, a domain to beevaluated for words unlikely to appear. The evaluation engine may selectthe domain based on information identifying the speech input. Forexample, in some embodiments an evaluation engine may be notified of adomain to which a speech input relates, or of multiple domains to whichthe speech input relates, by receiving information that identifies thespeech input. The notification of the domain(s) for a speech input mayinclude any suitable information and be at any suitable level ofspecificity. For example, in a medical context, a notification of one ormore domains may include information about a patient to which the speechinput relates (e.g., age, gender, etc.), information about a medicalprocedure to which the speech input relates, information about a medicalreport of which the speech input is a part, and/or information about apart of the medical report to which the speech input relates. Based onthis information, the evaluation engine may select one or more domainsto which the speech input relates and identify, based on each domain,words unlikely to appear in the speech input.

Some embodiments discussed above relate to evaluating words and/orphrases (including meanings of the words/phrases) included inrecognition results generated by an ASR system to determine whetherthere is an indication of a potential error in the recognition results.It should be appreciated, however, that all embodiments are not limitedto evaluating the words and/or phrases of recognition results todetermine whether there is an indication of a potential error. Someembodiments may additionally or alternatively use any other informationproduced by an ASR system based on speech input to determine whetherthere is an indication of a potential error.

Further, more embodiments are not limited to determining whether thereis an indication of a potential significant error that resulted from amisrecognition by an ASR system. Rather, as discussed above, someembodiments may determine whether a recognition result may contain apotential significant error that resulted from a speaker misspeaking,such as by the speaker speaking an incorrect word.

Applicants have recognized and appreciated that speech input may includea potential error when a speaker is uncertain of the speech input thather or she should be providing. Applicants have further recognized andappreciated that, in some cases, when a speaker is uncertain whether heor she is speaking correctly, the speaker may speak in a manner than isdifferent from the speaker's normal speech patterns, such as with anonstandard duration or with a nonstandard pitch. For example, when thespeaker is uncertain whether content of his or her speech is correct,the speaker may insert more pauses into his or her speech input, stretchsyllables or phonemes to longer durations than is typical, or speak witha higher than normal pitch or with a vocal fry.

Applicants have therefore recognized and appreciated that an evaluationof prosody information for speech input, which may include durationalinformation and pitch information, may provide an indication of whethera speaker was uncertain of the speech input that was provided and isbeing evaluated. Applicants have also recognized and appreciated thatwhen a speaker is detected as being uncertain of the speech input, thereis an increased likelihood that the speech input may include a potentialerror, so that it may be desirable to trigger an alert regardingrecognition results for the speech input such that a reviewer whoreviews the speech input can confirm that the speech input is correct.As discussed above, a reviewer may be more likely to carefully review arecognition result when the reviewer has been alerted that therecognition result may include a potential error.

FIG. 9 illustrates one process that may be implemented in someembodiments for evaluating prosody information produced by an ASR systembased on speech input to determine whether there is a potential error inthe speech input. The process 900 of FIG. 9 may be carried out by anevaluation engine based on output of an ASR system. Prior to the startof the process 900, speech input is received and processed by an ASRsystem, and the ASR system generates prosody information from analyzingthe speech input. The prosody information may be generated by the ASRsystem in any suitable manner, including using techniques that are knownin the art. In some cases, an ASR system may generate the prosodyinformation as a byproduct of performing a speech recognition processthat attempts to identify words and/or phrases included in the speechinput. In other cases, an ASR system may generate the prosodyinformation separately, rather than as part of the process of attemptingto identify words and/or phrases included in the speech input.

The process 900 begins in block 902, in which the evaluation enginereceives prosody information from the ASR system. The evaluation enginemay receive the prosody information in any suitable manner, includingvia any of the techniques discussed above in connection with FIG. 2. Forexample, the evaluation engine may receive the prosody informationdirectly from the ASR system or may retrieve the prosody informationfrom a data store in which prosody information generated by the ASRsystem was stored. In some embodiments, the evaluation engine mayreceive the prosody information along with other information produced bythe ASR system. For example, the evaluation engine may receive theprosody information from the ASR system together with recognitionresults that include words and/or phrases that the ASR system determinedmay have been included in the speech input.

In block 904, the evaluation engine evaluates the prosody informationand determines, based on the evaluation, a likelihood that the speakerwas uncertain of the speech input when the speaker provided the speechinput. The evaluation of block 904 may be carried out in any suitablemanner, as embodiments are not limited in this respect. In someembodiments, the evaluation engine may compare the prosody informationreceived in block 902 to reference prosody information. When the prosodyinformation is compared to the reference prosody information, anevaluation engine may determine whether variations from the referenceprosody information may be signs of potential uncertainty in thespeaker.

In embodiments in which prosody information is compared to referenceprosody information, the reference prosody information may be anysuitable information regarding prosody that is determined from anysuitable source. In some embodiments, the reference prosody informationmay be speaker specific, such as reference prosody informationdetermined for the particular speaker providing the speech input. Suchspeaker-specific reference prosody information may be determined in anysuitable manner, including by observing speech patterns of the speakerover time. In other embodiments, the reference prosody information maybe generic to multiple speakers. Such generic reference prosodyinformation may be generic to any suitable set of speakers, such as acategory of speakers sharing any suitable characteristic(s). Forexample, reference prosody information may be determined for a categoryof speakers that are “Americans,” or a category of speakers that are“men,” or a category of speakers that are “men from the American South.”When reference prosody information is identified for one or more sets ofspeakers, prosody information received in block 902 may be compared toreference prosody information for a set of speakers to which the speakerwho provided the speech input belongs.

Comparison of prosody information to reference prosody information maybe performed in any suitable manner. For example, the prosodyinformation for the speech input received in block 902 may includephoneme duration information that indicates a length of time that thespeaker used to pronounce phonemes included in the speech input.Reference prosody information may include reference phoneme durationinformation that identifies an expected pronunciation time for eachphoneme. When the evaluation engine compares the prosody information, insome cases the evaluation engine may determine that the phoneme durationinformation for the speech input indicates that pronunciation times forsome, most, or all of the phonemes included in the speech input islonger than the expected pronunciation times. When phoneme pronunciationtimes for one or more phonemes are longer than expected pronunciationtimes, the evaluation engine may determine that the speaker was speakingmore slowly than expected. Because a speaker may speak more slowly orhesitate when the speaker is uncertain, the longer pronunciation timesmay be a sign of potential uncertainty in the speaker. The evaluationengine may therefore determine that the speaker was uncertain.

As another example of the manner in which duration information may beused to detect potential uncertainty, prosody information received inblock 902 may include pause information identifying a length of one ormore pauses in the speech input, such as pauses between words. Pausesmay include filled pauses and/or unfilled pauses. Filled pauses mayinclude sounds expressed in speech input such as a speaker stretchingpronunciation of a word for the duration of the pause. A filled pausemay be detected by an ASR system as both a word and as a pause. Unfilledpauses may include a length of time included in speech input for whichthe speaker did not speak a word, such as a length of time during speechinput for which the speaker stayed silent or produced a hesitationvocalization. Reference prosody information may include information onlengths of normal pauses for speakers, such as information indicatingthe maximum or average length of a pause considered to be normal for aspeaker who is not uncertain. The evaluation engine may compare pauseinformation for the speech input to the reference pause information todetermine whether one or more pauses in the speech input are longer thanthe pauses considered to be normal. When one or more pauses in thespeech input are longer than normal, the evaluation engine may determinethat the speaker was speaking slowly or hesitating, which may be a signof potential uncertainty in the speaker.

As another example of the manner in which prosody information may beevaluated, the prosody information for the speech input received inblock 902 may include pitch information indicating one or more pitchesand/or more one more variations in pitch that were used to pronouncephonemes included in the speech input. Reference prosody information mayinclude reference pitch information for the phonemes, such as expectedpitches or variations in pitches for phonemes and/or unexpected pitchesor variations in pitches that are indicative of potential uncertainty.The pitch information for the speech input may be compared to thisreference pitch information to determine whether the pitch informationfor the speech input varies from what is expected or includes any of theunexpected pitches/variations. For example, a “vocal fry” is a commonvariation in pitch that may be considered to be a sign of potentialuncertainty for some American speakers. Reference prosody informationmay identify a vocal fry as an unexpected variation in pitch that isindicative of potential uncertainty. When the evaluation engine comparespitch information for the speech input to the reference pitchinformation and the pitch information for the speech input is determinedto include a vocal fry, the evaluation engine may determine that thespeaker was uncertain. As another example, a speaker who is hesitatingmay speak with a pitch that is higher than that speaker's typical pitch.Prosody information generated by an ASR system based on the speech inputmay indicate that the speaker was speaking with a higher-than-normalpitch. Reference prosody information may indicate that such a higherpitch is a sign of potential uncertainty. When the evaluation enginecompares the pitch information for the speech input to the referencepitch information, the evaluation engine may determine that, because thepitch used in the speech input was higher than normal for the speakerand the reference information indicates this is a sign of potentialuncertainty, the evaluation engine may determine that the speaker wasuncertain.

Speaker certainty may be detected based on factors other than durationof phonemes/pauses or pitch. In addition to or as an alternative tousing longer phonemes, inserting longer pauses, or speaking with anonstandard pitch, some speakers may utter hesitation vocalizations,such as “um” or “uh,” when uncertain of speech input. Prosodyinformation received in block 902 may include information on a number ofhesitation vocalizations included in the speech input and detected bythe ASR system. Because some ASR systems are adapted to removehesitation vocalizations from the words included in recognition resultswhen detected, these hesitation vocalizations may not be observable inwords and/or phrases of recognition results received from ASR systems.The ASR system may, however, produce information regarding hesitationvocalizations that are detected in a recognition result during arecognition process and provide this hesitation vocalization informationtogether with or as part of the prosody information.

In embodiments in which an evaluation engine evaluates hesitationvocalizations as part of evaluating speech input and comparesinformation about hesitation vocalizations of speech input to referencehesitation vocalization information, any suitable reference informationmay be used. For example, reference hesitation vocalization informationmay include a maximum or average number of hesitation vocalizationsincluded in speech input that is considered to be normal. As anotherexample, reference hesitation vocalization information may include amaximum or average length of a hesitation vocalization included inspeech input that is considered to be normal. The evaluation engine maycompare a count or length of hesitation vocalizations included in speechinput to a count or length of hesitation vocalizations indicated by thereference hesitation vocalization information to determine whether thecount is higher than is considered normal or a length of a hesitationvocalization is higher than is considered normal. When the count ofhesitation vocalizations or length of a hesitation vocalization includedin the speech input is higher than is considered normal, the evaluationengine may determine that the speaker was hesitating when speaking thespeech input, which may be a sign of potential uncertainty.

In embodiments that evaluate hesitation vocalizations, hesitationvocalization information and reference hesitation vocalization may bereceived and processed in any suitable manner. In some embodiments,information on hesitation vocalizations (e.g., counts of hesitationvocalizations) may be included in prosody information produced by an ASRsystem and received in block 902 and in reference prosody to which theprosody information is compared. In other embodiments, however,information on hesitation vocalizations may be stored as hesitationvocalization information separate from prosody information. In some suchembodiments, hesitation vocalization information may be received from anASR system in block 902 along with (but separate from) prosodyinformation and may be compared to reference hesitation vocalizationinformation. In embodiments that store and use hesitation vocalizationinformation separate from prosody information, hesitation vocalizationinformation (including reference hesitation vocalization information)may be produced in any suitable manner and may relate to any suitableone or more speakers, including in the manner discussed above inconnection with prosody information.

In some embodiments, prosody information and/or hesitation vocalizationinformation may include information generally relating to a speechinput, such as information that may be used to determine whether thespeech input includes any signs of potential uncertainty. For example,with respect to hesitation vocalizations, hesitation vocalizationinformation may include a count of hesitation vocalizations identifiedby the ASR system in speech input. However, embodiments are not limitedto operating with prosody information and/or hesitation vocalizationinformation that only generally relates to a speech input. In otherembodiments, prosody information and/or hesitation vocalizationinformation may additionally or alternatively include informationlinking signs of potential uncertainty to particular words and/orphrases of one or more recognition results produced by an ASR systembased on speech input. For example, with respect to hesitationvocalizations, the hesitation vocalization information may link one ormore hesitation vocalizations to words and/or phrases that the ASRsystem detected at similar locations in the speech input as thehesitation vocalization(s). For example, the hesitation vocalizationinformation may identify for a word of a recognition result that theword was recognized by the ASR system adjacent in the speech input to ahesitation vocalization detected by the ASR system. As another example,the hesitation vocalization information may identify for a phrase of arecognition result that a hesitation vocalization was detected by theASR system to be adjacent to or in the middle of the phrase in thespeech input. Prosody information may also include information linkingsigns of potential uncertainty to particular words and/or phrases ofrecognition results. For example, if an ASR system detects in speechinput a pause adjacent to a word and/or phrase, prosody information forthe speech input may identify that the pause is adjacent to thatword/phrase appearing in the recognition result. Identifying wordsand/or phrases of recognition results that appear at similar locationsin the speech input as signs of potential uncertainty may be useful inidentifying particular words and/or phrases appearing in recognitionresults in which the speaker may have been uncertain. This is becausethe speaker may have produced these signs of potential uncertainty closein time to particular words and/or phrases of which the speaker isuncertain. As discussed below, the evaluation engine may use informationidentifying these particular words and/or phrases to determine whetherto produce an alert regarding the words/phrases of recognition results.

Prosody information and/or hesitation vocalization information linkinghesitation vocalization information to words and/or phrases ofrecognition results may be formatted in any suitable manner, asembodiments are not limited in this respect. In some embodiments, theinformation may be formatted as a preliminary recognition resultproduced by an ASR system during a recognition process that was notfiltered to suppress information regarding hesitation vocalizations,information regarding pauses, or other information that may be used toidentify signs of potential uncertainty. As discussed above, an ASRsystem may be configured to process preliminary results of a recognitionprocess to suppress information that may be indicative of uncertaintywhen that information is detected by the ASR system, including bysuppressing information regarding hesitation vocalizations or pauses. Insome embodiments, however, such preliminary recognition results may beprovided by the ASR system to the evaluation engine. Because theinformation that may be indicative of uncertainty was not suppressed bythe ASR system and was received by the evaluation engine, thisinformation may appear in the preliminary recognition result alongsidewords and/or phrases detected by the ASR system in the speech input.When the evaluation engine receives the preliminary recognition resultin which this information has not been suppressed, the evaluation enginemay review the preliminary recognition result and the information toidentify the words and/or phrases that were detected by the ASR systemas being at similar locations in the speech input as the informationthat may be indicative of uncertainty of the speaker. As discussedbelow, if the evaluation engine determines, based on these signs ofuncertainty, that it is likely that the speaker was uncertain, theevaluation engine may raise an alert regarding these words and/orphrases.

In some embodiments, in addition to or as an alternative to determininguncertainty of a speaker based on signs of uncertainty identified fromprosody and/or hesitation vocalization information for one speech input,the evaluation engine may determine uncertainty of a speaker based onsigns of uncertainty identified from prosody and/or hesitationvocalization information for multiple speech inputs. For example, theevaluation engine may determine uncertainty based on an evaluation ofprosody and/or hesitation vocalization information for multiple speechinputs received together in time (e.g., as parts of a single dictationsession). Such an evaluation may be carried out by evaluating aggregatedprosody and/or hesitation vocalization information for the speechinputs. For example, with respect to hesitation vocalizations, anevaluation engine may determine uncertainty of a speaker by comparing acount of hesitation vocalizations detected by the ASR system in multiplespeech inputs to a count of a maximum or average number of hesitationvocalizations that is considered to be normal when included in speechinput over a length of time (e.g., 30 seconds or one minute). When anevaluation engine determines that the count of hesitation vocalizationsdetected in the multiple speech inputs is higher than is considered tobe normal, the evaluation engine may determine that the speaker wasuncertain when speaking each of the multiple speech inputs.

When the evaluation engine evaluates one or more speech inputs using anyone or more of these or any other suitable factors indicative ofpotential uncertainty in a speaker, the evaluation engine may calculatea likelihood of potential uncertainty of the speaker. The evaluationengine may calculate the likelihood in any suitable manner, asembodiments are not limited in this respect. For example, when only onefactor is evaluated and a likelihood of uncertainty is determined basedon that factor, this likelihood may be used as the likelihood of thespeaker's uncertainty. Alternatively, the evaluation engine maycalculate a likelihood of uncertainty in the speaker for each ofmultiple factors evaluated, and those likelihoods can be combined toproduce an overall likelihood of uncertainty of the speaker. Thelikelihoods produced based on each factor may be based on theinformation that uses that factor that is evaluated by the evaluationengine. For example, when phoneme duration information is evaluated, theevaluation engine may use an amount of variation of the phonemedurations from expected phoneme durations to calculate a numberindicating a likelihood of uncertainty in the speaker. As anotherexample, when a count of hesitation vocalizations is evaluated, theevaluation engine may use a variation of the count of hesitationvocalizations from the normal count of hesitation vocalizations tocalculate a number indicating likelihood of uncertainty in the speaker.Likelihoods based on each of the factors may then be combined in anysuitable manner, including using a weighting function, to produce alikelihood of uncertainty of the speaker.

Once the likelihood of uncertainty of the speaker is calculated, thelikelihood may be used in any suitable manner. For example, thelikelihood may be used in determining whether to raise an alert to areviewer regarding the recognition results that are based on the speechinput. By raising an alert when the speaker was uncertain, a reviewer towhich the alert is provided may be more likely to more closely reviewthe recognition results based on the speech input and may be more likelyto identify errors in the recognition results that may have been presentin the speech input due to the speaker's uncertainty.

Accordingly, the process 900 continues in block 906 to determine whetherthe likelihood calculated in block 904 indicates whether the speaker wassufficiently uncertain for an alert to be generated. The evaluationengine may make this determination in any suitable manner, an example ofwhich is using one or more thresholds. In the example of FIG. 9, inblock 906, the evaluation engine compares the likelihood of uncertaintycalculated in block 904 to a threshold likelihood to determine whetherthe calculated likelihood exceeds the threshold. If the calculatedlikelihood does not exceed the threshold, the speaker may be determinednot to have been sufficiently likely to have been uncertain for an alertto be raised. If, however, the evaluation engine determines that thecalculated likelihood exceeds the threshold, then the speaker may bedetermined to be sufficiently likely to have been uncertain for an alertto be raised. In block 908, therefore, the evaluation engine triggers analert regarding the uncertainty and stores information identifying thatthe speaker may have been uncertain. The evaluation engine may triggerthe alert in any suitable manner, including in the manner discussedabove in connection with block 208 of FIG. 2. The evaluation engine maystore any suitable information in block 908. The information mayinclude, for example, the calculated likelihood of uncertainty and/orinformation identifying the detected prosody information and/orhesitation vocalization information, reference prosody and/or hesitationvocalization information on which the determination of uncertainty wasbased, and/or particular words and/or phrases of one or more recognitionresults of which the speaker was determined to be uncertain.

Once the alert has been triggered and the information stored in block908, or if the speaker was determined in block 906 not to besufficiently uncertain to trigger an alert, the process 900 ends.Information regarding detected uncertainty of the speaker may be used inany suitable manner following the process 900. In some embodiments, whena speaker is detected to have been potentially uncertain in speechinput, an alert may be provided to a reviewer that is reviewingrecognition results based on that speech input, as discussed below inconnection with FIG. 10. In other embodiments, the information regardingpotential uncertainty may be used to determine whether to trigger analert when words and/or phrases of recognition results are evaluatedusing any of the exemplary techniques described above in connection withFIGS. 2-8. A process for weighting a likelihood of a word or phrasebeing a potential error based on consequences associated with it beingan error was discussed above in connection with FIG. 5. In someembodiments, a similar weighting process may be carried out usinginformation regarding potential uncertainty of a speaker. For example, alikelihood of a word or phrase appearing in a recognition result, or ofa difference in meaning appearing in recognition results in error, maybe weighted based at least in part on information indicating potentialuncertainty of the speaker. By doing so, when the speaker is detected asbeing uncertain of the speech input, any potential errors or potentialsignificant errors that are detected in recognition results may be morelikely to trigger an alert and therefore be more likely to be carefullyreviewed by a reviewer.

In embodiments that evaluate prosody and/or hesitation vocalizationinformation to determine a likelihood of uncertainty of a speaker, theinformation that is evaluated may be received from any suitable sourceat any suitable time. In some embodiments, the information may bereceived from an ASR engine immediately following a speech recognitionprocess conducted on speech input. In such embodiments, prosody and/orhesitation vocalization information may be evaluated as speech input isinput by a speaker and analyzed by the ASR engine. In other embodiments,prosody and/or hesitation vocalization information for speech input maybe evaluated at a later time, such as at a time following receipt ofother speech input and following processing of that other speech inputby the ASR system. For example, in some embodiments speech input may bereceived and processed by an ASR system and words and/or phrases ofrecognition results determined by the ASR system may be displayed via auser interface. A user (who may be a reviewer) may then edit the wordsand/or phrases in any suitable way (e.g., by providing additional speechinput and/or by providing textual input via a keyboard). Once thewords/phrases are edited, an evaluation of the words/phrases may becarried out to determine whether there are signs of potentialuncertainty or other indications of potential errors. Such an evaluationmay include evaluating prosody and/or hesitation vocalizationinformation regarding the speech input that resulted in thewords/phrases included following the editing. When the prosody and/orhesitation vocalization information is evaluated, the prosody and/orhesitation vocalization information may be information related to anysuitable speech input that was provided at any suitable time to producethe words/phrases included following the editing. When edits are made towords and/or phrases of an original speech input, some words or phrasesof the original speech input may be removed from recognition results.Prosody and/or hesitation vocalization information produced from theportions of the speech input relating to the removed words/phrases maytherefore not be relevant to an analysis of the words and/or phrasesincluded following the editing. Additionally, during the editing, newspeech input may be provided by a speaker to include new words/phrasesto replace words/phrases included in the original speech input. Prosodyand/or hesitation vocalization information from the new speech input maytherefore be relevant to the analysis of the words/phrases includedfollowing the editing. Accordingly, in some embodiments, when anevaluation of prosody and/or hesitation vocalization information iscarried out, the information that is evaluated may be informationproduced from multiple different speech inputs received at differenttimes that collectively resulted in the words/phrases included followingthe editing.

Techniques described herein may be used by an evaluation engine of aspeech processing system to review one or more recognition resultsgenerated by an ASR system for a speech input to determine whether therecognition results include any of potential errors, including errorsthat may change a meaning of a recognition result in a semanticallymeaningful way. As mentioned above, some embodiments may identifypotential errors to a reviewer (e.g., a human reviewer) so that theattention of the reviewer may be called to the recognition results thatmay include potential errors. The reviewer may then determine whetherthe recognition results in fact include an error and may take remedialaction if desired.

FIG. 10 illustrates one process that may be used in some embodiments bya review engine to present information on potential errors identified byan evaluation engine to a reviewer during a review process. The process1000 may be used by a review engine that displays recognition results toa reviewer for one or more speech inputs and that displays informationregarding one or more potential errors identified in the recognitionresult(s) and/or potential errors or potential uncertainty identified inspeech input on which the recognition result(s) is/are based. In someembodiments, multiple speech inputs and multiple potential errors may beevaluated together by the reviewer using the review engine. The speechinput(s) for which the recognition results may be viewed together may bespeech inputs that were received close together in time and/or that aregrouped together in some other manner, such as by relating to a sametopic and/or a same document. For example, when the speech inputs are adictation of a document, a reviewer may view together the recognitionresults for all speech input of the dictation, such that the reviewercan view the complete document at one time and can, at one time, reviewthe potential errors identified by an evaluation engine in therecognition results for the speech inputs of the document. In one reviewprocess, the recognition results for multiple speech inputs may bedisplayed along with annotations (e.g., flags) identifying locations ofpotential errors in the recognition results. A reviewer may review therecognition results and the annotations and, if the reviewer desires,view additional information about the potential errors, such asinformation produced and stored by evaluation techniques that anevaluation engine used to identify the potential errors.

The reviewer to which the recognition results and potential errors aredisplayed by the process 1000 may be any suitable entity capable ofreviewing the recognition results. In some embodiments, the reviewer maybe a person who provided the speech input for which the recognitionresults were produced (e.g., a physician dictating medical reports),while in other embodiments the reviewer may be a different person, suchas a transcriptionist or other person who reviews recognition resultsbut did not provide the speech input.

Prior to the start of the process 1000, an ASR system generatesrecognition results for one or more speech input and an evaluationengine evaluates those recognition results to determine whether thereare any indications of potential errors that may be significant. Whenthe evaluation engine identifies potential errors, the evaluation enginetriggers an alert, and in some embodiments optionally stores informationabout the potential error and/or about the recognition results fromwhich the evaluation engine determined there might be a potential errorin the recognition results.

The process 1000 begins in block 1002, in which a review engine receivesrecognition results generated by an ASR system for a speech input andoptionally receives information identifying potential errors determinedby an evaluation engine. The one or more recognition results include atop result that the ASR system is most confident is a correctrepresentation of the speech input, and may optionally also include oneor more alternative recognition results.

In block 1004, the review engine presents, for each speech input, thetop recognition result to the reviewer via a display. The top result maybe presented in any suitable manner. For example, the top result can bepresented so as to form a string of text of a document, such as wherethe speech inputs each form a portion of a dictation of a document.

In block 1006, the review engine determines whether an alert regarding apotential error in the recognition results was triggered by theevaluation engine. If an alert was triggered, then the review engine maynotify the reviewer in some way that a potential error was detected inthe recognition results. The reviewer may be notified in any suitablemanner, including through a graphical and/or textual message displayedto the reviewer and/or an audible sound produced for the reviewer, or inany other way.

A reviewer may additionally or alternatively be notified regarding thepotential error by an annotation made to the top result in block 1008.In block 1008, the review engine may annotate the top result based onthe information received in block 1002 so as to identify to the reviewerpotential errors that were detected by the evaluation engine. Theannotations may be made in any suitable manner. In some embodiments, adisplay style of the top result may be altered in some way so as toidentify a location of a potential error. For example, where the displayof the top recognition result includes a text display of words of thetop recognition result, a font style in the display of words and/orphrases that correspond to the potential errors identified by theevaluation engine may be altered. An altering of the font style mayinclude changing a text color, a background color for the text, a fontsize, a font weight (e.g., bold or italic), or any other font property.As another example, a word or phrase that corresponds to a potentialerror may be annotated with a graphic, such as by placing near the wordor phrase an icon indicating that the word or phrase is associated witha potential error. Any suitable icon may be used, including a stylizedline placed under the word or phrase. Rather than (or in addition to)altering a display style of the top result, in some embodiments the topresult may be annotated through the inclusion of information regardingalternative recognition results associated with the potential errors.For example, where a potential error was identified based on adifference between the top result and an alternative recognition result,near where the top result is displayed some content of the alternativerecognition result may be displayed. The content of the alternativerecognition result may be displayed in a different manner in someembodiments, such as in a different font style (e.g., different fontcolor, different background color, different weight, etc.). For example,if a top result for a speech input is the phrase “there is evidence ofactive disease in the abdomen” and a potential error was identifiedbased on the alternative recognition result “there is no evidence ofactive disease in the abdomen,” the top result may be annotated by theinclusion in the display of the word “no” in a different font stylebetween the “is” and “evidence” of the top result. This is merely oneexample as it should be appreciated that any suitable annotations may beused, as embodiments are not limited in this respect.

In some embodiments, annotations regarding potential errors identifiedfor a word, phrase, sentence, paragraph, or other portion of arecognition result may be combined in the display to yield fewerannotations (e.g., a single annotation) for the portion of therecognition result. The annotations may be combined in any suitable wayfor any suitable reason. Combining the annotations may serve to limitthe number of annotations that may be displayed to a reviewer, which mayaid the reviewer in focusing on reviewing the identified potentialerrors and may prevent the reviewer from being overwhelmed by a largenumber of annotations and potential errors to review. For example, whenan evaluation engine identifies many potential errors in recognitionresults for a sentence and the sentence is to be displayed for review, areview engine may display a single annotation for the sentence thatidentifies to the reviewer that the entire sentence is potentiallyerroneous and should be reviewed carefully. A reviewer may find thereviewing process easier when the reviewer need only focus on a fewerrors, and may therefore find the sentence easier to review when theentire sentence is annotated once, rather than the sentence beingannotated multiple times for multiple potential errors.

After the top result has been annotated with the indication(s) of thepotential error(s) in some embodiments, a reviewer may interact with theresults in the display during the review process. In particular, thereviewer may interact with those results that have been annotated asbeing associated with potential errors and may request additionalinformation regarding the potential errors. In block 1010, the reviewengine may respond to the reviewer's request(s) for informationregarding potential errors with additional information regarding thepotential error. Any suitable information regarding a potential errormay be displayed. In some embodiments, for example, the additionalinformation may include one or more alternative recognition results fromwhich the potential error was identified. In other embodiments, theadditional information may include one or more words of an alternativerecognition result, rather than an entire recognition result. Theadditional information may also include information on the type ofpotential error identified, such as inconsistent meanings, differencesin words, or words that were identified as unlikely to appear.Embodiments are not limited to displaying any particular type ofadditional information, and may display any suitable information thatmay aid a reviewer in determining whether the recognition resultsinclude an error, which may include any information that may aid thereviewer in correcting the error.

In some cases, a potential error may have been identified throughmultiple different evaluation techniques carried out on recognitionresults, such as being identified by a semantic interpretation processcarried out by an evaluation facility and by a direct word comparisoncarried out by the evaluation facility. Each of the evaluationtechniques may have produced and stored information about the potentialerror and this information may be presented to the reviewer asadditional information about the potential error. When a potential errorhas been identified through multiple different techniques, any suitableadditional information about the potential error may be displayed to thereviewer upon request. In some embodiments, information about thepotential error produced by all of the multiple techniques by which thepotential error was identified may be provided to the reviewer. In otherembodiments, to limit the information provided to the reviewer so as notto overwhelm the reviewer, additional information about the potentialerror may be presented to the reviewer that was produced by only one orsome of the evaluation techniques. For example, additional informationmay be presented that was produced by the evaluation technique thatidentified the potential error first in time. As another example, thereview engine may be configured with a ranking of evaluation techniquesand may select, from the multiple evaluation techniques that identifiedthe potential error, the evaluation technique that is highest in theranking, and additional information that was produced by the selectedevaluation technique may be presented to the reviewer. Where such aranking is used, the ranking of evaluation techniques may be producedbased on any suitable criterion or criteria, including a developer'simpressions of, or information about, reliability of the evaluationtechniques. It should be appreciated that embodiments are not limited tomaking a selection between evaluation techniques or only displayingadditional information produced by one or some evaluation techniques,and that embodiments that make a selection are not limited to making theselection in any particular manner.

Once the additional information has been presented to the reviewer, orif the review engine determines in block 1006 that no alert wastriggered by the evaluation engine, the process 1000 ends. Following theprocess 1000 and after a reviewer reviews the recognition results andcorrects potential errors, the recognition results may be finalized inany suitable way.

Various examples given above were discussed in connection withevaluating speech in a medical context, such as speech dictated by aclinician that describes a clinical encounter with a patient. Some ofthe examples were discussed in connection with a radiologist or otherclinician discussing the results of a radiological examination of apatient. It should be appreciated, however, that embodiments are notlimited to evaluating speech in a medical context or any otherparticular domain and that the techniques described herein may be usedto evaluate speech in any suitable domain or domains.

The techniques described herein may be implemented in any suitablemanner. Included in the discussion above are a series of flow chartsshowing the acts of various processes. These processes may beimplemented as software integrated with and directing the operation ofone or more single- or multi-purpose processors, may be implemented ashardware circuits such as a Digital Signal Processing (DSP) circuit oran Application-Specific Integrated Circuit (ASIC), or may be implementedin any other suitable manner. It should be appreciated that the flowcharts included herein do not depict the syntax or operation of anyparticular circuit or of any particular programming language or type ofprogramming language. It should also be appreciated that, unlessotherwise indicated herein, the particular sequence of steps and/or actsdescribed in each flow chart is merely illustrative, as the processesand techniques described herein can be implemented in other ways.

In some embodiments, the techniques described herein may be embodied incomputer-executable instructions implemented as software, including asapplication software, system software, firmware, middleware, embeddedcode, microcode, or any other suitable type of computer code. Suchcomputer-executable instructions may be written using any suitableprogramming languages and/or programming or scripting tools, and alsomay be compiled as executable machine language code or intermediate codethat is executed on a framework or virtual machine.

When techniques described herein are embodied as computer-executableinstructions, these computer-executable instructions may be implementedin any suitable manner, including as a number of functionalityfacilities, each providing one or more operations to complete executionof algorithms operating according to these techniques. A “functionalityfacility,” however instantiated, is a structural component of a computersystem that, when integrated with and executed by one or more computers,causes the one or more computers to perform a specific operational role.Functionality facilities may include routines, programs, objects,components, data structures, etc. that perform particular tasks orimplement particular abstract data types. The functionality of thefunctionality facilities may be combined or distributed as desired inthe systems in which they operate. In some implementations, one or morefunctionality facilities carrying out techniques herein may togetherform a complete software package. These functionality facilities may, inalternative embodiments, be adapted to interact with other, unrelatedfunctionality facilities and/or processes, to implement a softwareprogram application.

Some exemplary “engines” have been described herein for carrying out oneor more tasks. In some embodiments, an engine may be implemented as oneor more functionality facilities executing on one or more processors orable to execute on one or more processors. Embodiments that implementengines as one or more functionality facilities are not limited to beingimplemented in any specific number, division, or type of functionalityfacilities. In some embodiments, all functionality may be implemented ina single functionality facility.

Computer-executable instructions implementing the techniques describedherein (when implemented as one or more functionality facilities or inany other manner) may, in some embodiments, be encoded on one or morecomputer-readable media to provide functionality to the media.Computer-readable media may include magnetic media such as a hard diskdrive, optical media such as a Compact Disk (CD) or a Digital VersatileDisk (DVD), a persistent or non-persistent solid-state memory (e.g.,Flash memory, Magnetic RAM, etc.), or any other suitable storage media.Such a computer-readable medium may be implemented in any suitablemanner, including as computer-readable storage media 1106 of FIG. 11described below (i.e., as a portion of a computing device 1100) or as astand-alone, separate storage medium. As used herein, “computer-readablemedia” (also “computer-readable storage media”) refers to tangiblestorage media. Tangible storage media are non-transitory and have atleast one physical, structural component. In a “computer-readablemedium,” as used herein, at least one physical, structural component hasat least one physical property that may be altered in some way during aprocess of creating the medium with embedded information, a process ofrecording information thereon, or any other process of encoding themedium with information. For example, a magnetization state of a portionof a physical structure of a computer-readable medium may be alteredduring a recording process.

In some, but not all, implementations in which the techniques may beembodied as computer-executable instructions, these instructions may beexecuted on one or more computing device(s) operating in any suitablecomputer system, or one or more computing devices (or one or moreprocessors of one or more computing devices) may be programmed toexecute the computer-executable instructions. A computing device orprocessor may be programmed to execute instructions stored in any manneraccessible to the computing device/processor. Functionality facilitiesthat comprise these computer-executable instructions may be integratedwith and direct the operation of a single multi-purpose programmabledigital computer apparatus, a coordinated system of two or moremulti-purpose computer apparatuses sharing processing power and jointlycarrying out the techniques described herein, a single computerapparatus or coordinated system of computer apparatuses (co-located orgeographically distributed) dedicated to executing the techniquesdescribed herein, one or more Field-Programmable Gate Arrays (FPGAs) forcarrying out the techniques described herein, or any other suitablesystem. Accordingly, in some embodiments, techniques described hereinmay be implemented as systems executing on a distributed system of twoor more computing devices. For example, an evaluation engine operatingaccording to techniques described above may be executed by two or morecomputing devices.

FIG. 11 illustrates one exemplary implementation of a computing devicein the form of a computing device 1100 that may be used in a systemimplementing the techniques described herein, although others arepossible. It should be appreciated that FIG. 11 is intended neither tobe a depiction of necessary components for a computing device to operatein accordance with the principles described herein, nor a comprehensivedepiction.

Computing device 1100 may comprise at least one processor 1102, anetwork adapter 1104, and computer-readable storage media 1106.Computing device 1100 may be, for example, a desktop or laptop personalcomputer, a personal digital assistant (PDA), a smart mobile phone, aserver, or any other suitable computing device. Network adapter 1104 maybe any suitable hardware and/or software to enable the computing device1100 to communicate wired and/or wirelessly with any other suitablecomputing device over any suitable computing network. Computer-readablemedia 1106 may be adapted to store data to be processed and/orinstructions to be executed by processor 1102.

The data and instructions stored on the one, two, or morecomputer-readable storage media 1106 may comprise computer-executableinstructions implementing techniques described herein. In the example ofFIG. 11, computer-readable storage media 1106 stores computer-executableinstructions implementing various engines and storing variousinformation as described above. Computer-readable storage media 1106 maystore an ASR facility 1108 that, when executed on the processor(s) 1102,implements an ASR system for carrying out a speech recognition processon data regarding a speech input to generate one or more recognitionresults for the speech input. Computer-readable storage media 1106 mayalso store an evaluation facility 1110 that, when executed on theprocessor(s) 1102, implements an evaluation engine that carries out anevaluation of recognition results, using any of the exemplary techniquesdescribed herein. As part of evaluating the recognition results, in someembodiments an evaluation engine may interact with a semanticinterpretation engine. In such embodiments, a semantic interpretationfacility 1112 may be stored on the computer-readable storage media 1106.The semantic interpretation facility 1112, when executed by theprocessor(s) 1102, implements a semantic interpretation engine thatcarries out a semantic interpretation of one or more recognition resultsto determine facts expressed in the recognition results. Also, inembodiments where one or more sets of words and/or phrases are evaluatedby an evaluation engine, sets of words/phrases 1114 may be stored on thecomputer-readable storage media 1106. The sets of words/phrases 1114 mayinclude any of the sets of words discussed above. As should beappreciated from the above discussion of techniques that may be used byan evaluation engine, in some embodiments the evaluation engine may notuse both a semantic interpretation engine and sets of words/phrases1114. Accordingly, in some embodiments the computer-readable storagemedia 1106 may not store both the semantic interpretation facility 1112and the sets of words/phrases 1114, but would store the one used by theevaluation engine 1112. Additionally, while the semantic interpretationfacility 1112 is illustrated as separate from the evaluation facility1110, in some embodiments the semantic interpretation facility 1112 mayform a part of the evaluation facility 1110. The computer-readablestorage media 1106 may further store, in some embodiments, prosodyand/or hesitation vocalization information 1116. Prosody and/orhesitation vocalization information 1116 may include any suitableinformation regarding prosody and/or hesitation vocalizations. Theinformation 1116 may include prosody and/or hesitation vocalizationinformation produced by an ASR system based on speech input provided bya speaker and/or reference prosody and/or hesitation information thatmay be used by the evaluation facility 1110 in evaluating informationproduced by an ASR system. In some embodiments that store both prosodyinformation and hesitation vocalization information, the prosodyinformation may be stored together with hesitation vocalizationinformation (e.g., stored in a single logical unit) or may be storedseparately in any suitable manner. In some embodiments, thecomputer-readable storage media 1106 may additionally store a pairingfacility 1118, which when executed by the processor(s) 1102 implements apairing engine to carry out a process for automatically determiningpairs of words to use in evaluating recognition results. Lastly, in someembodiments the computer-readable storage media 1106 may also store areview facility 1120 that, when executed by the processor(s) 1102,implements a review engine to carry out a review process for interactingwith a reviewer to display recognition results and information regardingpotential errors in the recognition results. While the example of FIG.11 illustrates multiple facilities 1108, 1110, 1112, 1118, and 1120,embodiments are not limited to implementing all of these facilities orimplementing all of these facilities together on one computing device.Rather, embodiments may implement any suitable facility or combinationof facilities in any suitable system including one or more devices.

Embodiments have been described where the techniques are implemented incircuitry and/or computer-executable instructions. It should beappreciated that some embodiments may be in the form of a method, ofwhich at least one example has been provided. The acts performed as partof the method may be ordered in any suitable way. Accordingly,embodiments may be constructed in which acts are performed in an orderdifferent than illustrated, which may include performing some actssimultaneously, even though shown as sequential acts in illustrativeembodiments.

Use of ordinal terms such as “first,” “second,” “third,” etc., in theclaims to modify a claim element does not by itself connote anypriority, precedence, or order of one claim element over another or thetemporal order in which acts of a method are performed, but are usedmerely as labels to distinguish one claim element having a certain namefrom another element having a same name (but for use of the ordinalterm) to distinguish the claim elements.

Also, the phraseology and terminology used herein is for the purpose ofdescription and should not be regarded as limiting. The use of“including,” “comprising,” “having,” “containing,” “involving,” andvariations thereof herein, is meant to encompass the items listedthereafter and equivalents thereof as well as additional items.

The word “exemplary” is used herein to mean serving as an example,instance, or illustration. Any embodiment, implementation, process,feature, etc. described herein as exemplary should therefore beunderstood to be an illustrative example and should not be understood tobe a preferred or advantageous example unless otherwise indicated.

Having thus described several aspects of at least one embodiment, it isto be appreciated that various alterations, modifications, andimprovements will readily occur to those skilled in the art.Accordingly, the foregoing description and drawings are by way ofexample only.

The invention claimed is:
 1. A method of processing a result of arecognition by an automatic speech recognition (ASR) system on a speechinput, the method comprising: determining whether the result of therecognition by the ASR system includes a potential recognition error,wherein determining whether the result of the recognition includes thepotential recognition error comprises: determining whether a word orphrase of the result of the recognition is uncommon in a domain to whichthe speech input relates, wherein determining whether the word or phraseof the result of the recognition is uncommon in the domain to which thespeech input relates comprises: determining, using a language model forthe domain to which the speech input relates, a likelihood of the wordor phrase of the result of the recognition appearing in the domain towhich the speech input relates, wherein: the language model for thedomain to which the speech input relates includes for each word orphrase of a plurality of words or phrases, a value indicating alikelihood of the word or phrase appearing in the domain to which thespeech input relates, and determining the likelihood comprisesevaluating the word or phrase of the result of the recognition using thelanguage model to determine a value indicating the likelihood of theword or phrase of the result of the recognition appearing in the domainto which the speech input relates; determining whether the valueindicating the likelihood of the word or phrase of the result of therecognition appearing in the domain is below a threshold; and inresponse to determining that the value indicating the likelihood of theword or phrase of the result of the recognition appearing in the domainis below the threshold, determining that the word or phrase of theresult of the recognition is uncommon in the domain to which the speechinput relates, and triggering an alert indicating that the resultincludes the potential recognition error.
 2. The method of claim 1,further comprising: comparing at least one second word or phrase in theresult to a set of words and/or phrases that includes words and/orphrases that are uncommon in the domain; and in response to determiningthat the at least one second word or phrase in the result appears in theset, determining that the at least one second word or phrase is uncommonin the domain to which the speech input relates.
 3. The method of claim1, wherein evaluating the word or phrase of the result using thelanguage model for the domain comprises evaluating the word or phraseusing a language model for the domain that indicates probabilities ofoccurrence of words and/or phrases in speech or text related to thedomain.
 4. The method of claim 1, further comprising: determining thedomain to which the speech input relates based on informationidentifying the speech input.
 5. The method of claim 1, wherein: thespeech input relates to a plurality of domains, and the method furthercomprises: determining a set of words and/or phrases that are uncommonin at least a first domain and a second domain of the plurality ofdomains to which the speech input relates, and determining whether theword or phrase of the result is uncommon in either the first domain orthe second domain to which the speech input relates, wherein determiningwhether the word or phrase of the result is uncommon in either the firstdomain or the second domain comprises determining whether the word orphrase of the result is included in the set.
 6. The method of claim 1,wherein the determining whether the word or phrase of the result of therecognition is uncommon in the domain comprises determining whether theresult comprises a word or phrase that is uncommon in speech inputrelating to a medical domain.
 7. At least one non-transitorycomputer-readable storage medium having encoded thereon executableinstructions that, when executed by at least one processor, cause the atleast one processor to carry out a method of processing a result of arecognition by an automatic speech recognition (ASR) system on a speechinput, the method comprising: determining whether the result of therecognition by the ASR system includes a potential recognition error,wherein determining whether the result of the recognition includes thepotential recognition error comprises: determining whether a word orphrase of the result of the recognition is uncommon in a domain to whichthe speech input relates, wherein determining whether the word or phraseof the result of the recognition is uncommon in the domain to which thespeech input relates comprises: determining, using a language model forthe domain to which the speech input relates, a likelihood of the wordor phrase of the result of the recognition appearing in the domain towhich the speech input relates, wherein: the language model for thedomain to which the speech input relates includes for each word orphrase of a plurality of words or phrases, a value indicating alikelihood of the word or phrase appearing in the domain to which thespeech input relates, and determining the likelihood comprisesevaluating the word or phrase of the result of the recognition using thelanguage model to determine a value indicating the likelihood of theword or phrase of the result of the recognition appearing in the domainto which the speech input relates; determining whether the valueindicating the likelihood of the word or phrase of the result of therecognition appearing in the domain is below a threshold; and inresponse to determining that the value indicating the likelihood of theword or phrase of the result of the recognition appearing in the domainis below the threshold, determining that the word or phrase of theresult of the recognition is uncommon in the domain to which the speechinput relates, and triggering an alert indicating that the resultincludes the potential recognition error.
 8. The at least onenon-transitory computer-readable storage medium of claim 7, wherein themethod further comprises: comparing at least one second word or phrasein the result to a set of words and/or phrases that includes wordsand/or phrases that are unlikely to appear in the domain; and inresponse to determining that the at least one second word or phrase inthe result appears in the set, determining that the at least one secondword or phrase is unlikely to occur in the domain to which the speechinput relates.
 9. The at least one non-transitory computer-readablestorage medium of claim 7, wherein evaluating the word or phrase of theresult using the language model for the domain comprises evaluating theword or phrase using a language model for the domain that indicatesprobabilities of occurrence of words and/or phrases in speech or textrelated to the domain.
 10. The at least one non-transitorycomputer-readable storage medium of claim 7, further comprising:determining the domain to which the speech input relates based oninformation identifying the speech input.
 11. The at least onenon-transitory computer-readable storage medium of claim 7, wherein: thespeech input relates to a plurality of domains, and the method furthercomprises: determining a set of words and/or phrases unlikely to occurin at least a first domain or a second domain of the plurality ofdomains to which the speech input relates, and determining whether theword or phrase of the result is unlikely to occur in either the firstdomain or the second domain to which the speech input relates, whereindetermining whether the word or phrase of the result is unlikely tooccur in either the first domain or the second domain comprisesdetermining whether the word or phrase of the result is included in theset.
 12. The at least one non-transitory computer-readable storagemedium of claim 7, wherein the determining whether the word or phrase ofthe result of the recognition is uncommon in the domain comprisesdetermining whether the result comprises a word or phrase that isuncommon in speech input relating to a medical domain.
 13. An apparatuscomprising: at least one processor; and at least one storage mediumhaving encoded thereon executable instructions that, when executed by atleast one processor, cause the at least one processor to carry out amethod of processing a result of a recognition by an automatic speechrecognition (ASR) system on a speech input, the method comprising:determining whether the result of the recognition by the ASR systemincludes a potential recognition error, wherein determining whether theresult of the recognition includes the potential recognition errorcomprises: determining whether a word or phrase of the result of therecognition is uncommon in a domain to which the speech input relates,wherein determining whether the word or phrase of the result of therecognition is uncommon in the domain to which the speech input relatescomprises: determining, using a language model for the domain to whichthe speech input relates, a likelihood of the word or phrase of theresult of the recognition appearing in the domain to which the speechinput relates, wherein:  the language model for the domain to which thespeech input relates includes for each word or phrase of a plurality ofwords or phrases, a value indicating a likelihood of the word or phraseappearing in the domain to which the speech input relates, and determining the likelihood comprises evaluating the word or phrase ofthe result of the recognition using the language model to determine avalue indicating the likelihood of the word or phrase of the result ofthe recognition appearing in the domain to which the speech inputrelates; determining whether the value indicating the likelihood of theword or phrase of the result of the recognition appearing in the domainis below a threshold; and in response to determining that the valueindicating the likelihood of the word or phrase of the result of therecognition appearing in the domain is below the threshold, determiningthat the word or phrase of the result of the recognition is uncommon inthe domain to which the speech input relates, and triggering an alertindicating that the result includes the potential recognition error. 14.The apparatus of claim 13, wherein the method further comprises:comparing at least one second word or phrase in the result to a set ofwords and/or phrases that includes words and/or phrases that areunlikely to appear in the domain; and in response to determining thatthe at least one second word or phrase in the result appears in the set,determining that the at least one second word or phrase is unlikely tooccur in the domain to which the speech input relates.
 15. The apparatusof claim 13, wherein evaluating the word or phrase of the result usingthe language model for the domain comprises evaluating the word orphrase using a language model for the domain that indicatesprobabilities of occurrence of words and/or phrases in speech or textrelated to the domain.
 16. The apparatus of claim 13, furthercomprising: determining the domain to which the speech input relatesbased on information identifying the speech input.
 17. The apparatus ofclaim 13, wherein: the speech input relates to a plurality of domains,and the method further comprises: determining a set of words and/orphrases unlikely to occur in at least a first domain and a second domainof the plurality of domains to which the speech input relates, anddetermining whether the word or phrase of the result is unlikely tooccur in either the first domain or the second domain to which thespeech input relates, wherein determining whether the word or phrase ofthe result is unlikely to occur in either the first domain or the seconddomain comprises determining whether the word or phrase of the result isincluded in the set.
 18. The apparatus of claim 13, wherein thedetermining whether the word or phrase of the result of the recognitionis unlikely to occur in the domain comprises determining whether theresult comprises a word or phrase that is unlikely to occur in speechinput relating to a medical domain.